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  <journal-id journal-id-type="publisher-id">46</journal-id>
  <journal-id journal-id-type="short-title">gssr</journal-id>
  <journal-id journal-id-type="doi">10.31703/gssr</journal-id>
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    <journal-title>Global Social Sciences Review</journal-title>
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  <issn publication-format="print">2520-0348</issn>
  <issn publication-format="electronic">2616-793X</issn>
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    <publisher-name>Humanity Publications</publisher-name>
    <publisher-loc>Pakistan</publisher-loc>
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<article-meta>
  <article-id pub-id-type="publisher-id">396034</article-id>
  <article-id pub-id-type="doi">10.31703/gssr.2025(X-IV).18</article-id>
  <article-id pub-id-type="other" specific-use="submission-id">6501</article-id>
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      <subject>article</subject>
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  <title-group>
    <article-title xml:lang="en">Workforce Capacity Constraints and Service Delivery in U.S. Community-Based Human-Service Organizations: A Scoping Review and Secondary Analysis of National Data</article-title>
  </title-group>
<contrib-group>
  <contrib contrib-type="author" seq="1" corresp="yes">
    <name>
      <surname>Fatima</surname>
      <given-names>Nudrat</given-names>
    </name>
    <role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Conceptualization" vocab-term-identifier="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
    <role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing – original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing – original draft</role>
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    <xref ref-type="corresp" rid="cor1"/>
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  <aff id="aff1">
    <label>1</label>
    <institution-wrap>
      <institution>MS in Management, Mount Vernon Nazarene University, Mount Vernon, Ohio</institution>
    </institution-wrap>
    <addr-line>United States (US)</addr-line>
    <country>United States</country>
  </aff>
</contrib-group>
<author-notes>
  <corresp id="cor1">Corresponding Author: Nudrat Fatima, MS in Management, Mount Vernon Nazarene University, Mount Vernon, Ohio, United States (US).</corresp>
<fn fn-type="COI-statement" id="fn-coi">
  <p>The authors declare that they have no conflicts of interest.</p>
</fn>
<fn fn-type="ethics-statement" id="fn-ethics">
  <p>This study did not require formal ethics approval.</p>
</fn>
<fn fn-type="data-availability-statement" id="fn-data">
  <p>Data sharing is not applicable to this article.</p>
</fn>
</author-notes>
<pub-date pub-type="epub" date-type="pub" publication-format="electronic">
  <day>31</day>
  <month>12</month>
  <year>2025</year>
</pub-date>
<pub-date pub-type="collection">
  <month>12</month>
  <year>2025</year>
</pub-date>
<pub-date date-type="pub" publication-format="print">
  <day>28</day>
  <month>11</month>
  <year>2025</year>
</pub-date>
  <volume>10</volume>
  <issue>4</issue>
  <season>Fall</season>
  <fpage>218</fpage>
  <lpage>229</lpage>
  <history>
    <date date-type="accepted">
      <day>28</day>
      <month>11</month>
      <year>2025</year>
    </date>
  </history>
<funding-group>
  <funding-statement>
<p>The authors received no specific funding for this work.</p>
  </funding-statement>
</funding-group>
<permissions>
  <copyright-year>2025</copyright-year>
  <copyright-holder>Humanity Publications</copyright-holder>
  <license license-type="open-access" xml:lang="en" xlink:href="https://creativecommons.org/licenses/by/4.0/">
    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License.</license-p>
  </license>
</permissions>
<self-uri content-type="text/html" xlink:href="https://gssrjournal.com/article/workforce-capacity-constraints-and-service-delivery-in-us-communitybased-humanservice-organizations-a-scoping-review-and-secondary-analysis-of-national-data"/>
<self-uri content-type="pdf" xlink:href="https://gssrjournal.com/pdf/gssr/CmD6X6lKhl.pdf"/>
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  <abstract>
    <p>Community-based human-service organizations play an important role in the U.S. social safety net, and delivering services to clients depends on the availability of workers. This study analyzes 38 related studies (2015-2026) and national labor data on broadening occupations, the effects of turnover, organizational responses, and capacity-planning gaps. Between 2012 and 2022, community and social-service occupations will experience ~293,400 annual job openings, yet vacancy rates will exceed 30% in key areas. There is a strong positive relationship between key drivers of turnover, such as intention to leave, compensation, caseload, and burnout, and longer service waitlists. We propose a workforce-capacity service-delivery model that integrates workforce supply, workload, and organizational capacity with service outcomes. At the end of the day, workforce shortages are not merely an HR problem but a structural problem for the availability of services, which can only be solved by coordinated policy, funding, and managerial interventions.</p>
  </abstract>
<kwd-group kwd-group-type="author-keywords">
  <kwd>Nonprofit Workforce</kwd>
  <kwd>Human Services</kwd>
  <kwd>Staff Turnover</kwd>
  <kwd>Service Capacity</kwd>
  <kwd>Burnout</kwd>
  <kwd>Retention</kwd>
  <kwd>Resource Allocation</kwd>
  <kwd>Community-based Organizations</kwd>
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<body>
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<sec id="sec-1"><title>Introduction</title></sec><p>Community-based organizations (CBOs) and nonprofit human-service providers provide a significant portion of the care and counseling, housing, nutrition, and family supports that form the foundation of community wellbeing in the United States. The service capacity of these organisations is very dependent on the availability and stability of their workforces as they are located in the interface of public funding streams and vulnerable populations. In recent years, providers have reported experiences of acute and chronic staffing shortages that result in longer waitlists, more limited services provided, and sometimes even programs are closed entirely (National Council of Nonprofits, <xref ref-type="bibr" rid="ref173956">2023</xref>; Center for an Urban Future, <xref ref-type="bibr" rid="ref173948">2023</xref>). These factors have brought back the human-service workforce as a variable for service availability, not just administrative issues.</p><p>Federal labor market projections are a reflection of the scale of the challenge. Community and social-service occupations are projected to experience over 293,400 job openings each year over the 2025-2035 decade due to employment growth and job turnover (U.S. Bureau of Labor Statistics, 2025). Demand is growing at the same time as an aging population, a growing behavioral-health need, and the ongoing transformation of publicly funded services to nonprofit-driven delivery. However, qualified workers are not plentiful because of low wages, emotional strain on the job and limited promotions, which results in turnover that is much higher than in other jobs of similar kind (Kim &amp; Kao, <xref ref-type="bibr" rid="ref173951">2014</xref>; McFadden et al., <xref ref-type="bibr" rid="ref173953">2015</xref>; Boyas et al., <xref ref-type="bibr" rid="ref173947">2012</xref>).</p><p>Many studies have been conducted on turnover, burnout, HR practices and organizational capacity and these areas have generally followed their own trajectory. Turnover research typically does not consider the measurable service outputs of a business or service, and organizational capacity studies rarely include workforce supply and demand dynamics (Walters, <xref ref-type="bibr" rid="ref173960">2020</xref>; Andersson et al., <xref ref-type="bibr" rid="ref173946">2016</xref>; Hasenfeld, <xref ref-type="bibr" rid="ref173949">2010</xref>). This leads to a disorganized evidence base with some good evidence on drivers of turnover but not on more comprehensive models that connect workforce supply, workload, demand and service capacity. This is an article that addresses that gap. It incorporates a literature review that sums up the findings of the 2015-2026 literature with secondary analysis of national data to give a combined picture of workforce capacity limitations and impact on service delivery.</p><p>Research questions include: (1) What are the workforce pressures of U.S. community and social-service organizations? (2) What are the jobs that are growing in demand? (3) What are the implications of a lack of staff and staff turnover on capacity to provide services? (4) What are the current practices in operation to control these stresses? What are the deficiencies in the workforce-capacity planning process? In the context of this article, these questions are meant to be tackled together, in order to shift the focus from the problem of workforce shortages to a structural problem that must be dealt with through coordinated policies, funding, and management.</p>
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<fig id="fig-1"><alt-text>Figure 1. Identification and screening of literature for the scoping review.</alt-text><caption><title>Figure 1. Identification and screening of literature for the scoping review.</title></caption><graphic xlink:href="https://gssrjournal.com/CmD6X6lKhl/imageoHssChhA.png"/></fig>
<sec id="sec-4">
<sec id="sec-3"><title>Related Work</title></sec><p>Studies of child-welfare and social-work environments have long been the focus of human-service turnover research because of the high turnover rates and the critical nature of the work that is done. The existing meta-analysis by Mor Barak and colleagues found that intention to leave, available employment options, job dissatisfaction, burnout, and low organizational support are the most consistent of child-welfare, social-work, and other human-service workers&apos; turnover antecedents (Mor Barak et al., <xref ref-type="bibr" rid="ref173955">2001</xref>). Later meta-analytic research further supported these impacts, with organizational commitment and job satisfaction the strongest predictors of intention to leave, and much less surprisingly, caseload was the least impactful of the factors considered, and demographic factors had mixed impacts (Kim &amp; Kao, <xref ref-type="bibr" rid="ref173951">2014</xref>).</p><p>A parallel literature on burnout identifies the decline in workers&apos; wellbeing as stemming from chronic emotional demands, overload in roles and inadequate resources. The multi-dimensional view of burnout, comprising emotional exhaustion, depersonalization and decreased personal accomplishment, continues to be the most accepted theory for the link between chronic work stress and withdrawal and exit (Maslach et al., <xref ref-type="bibr" rid="ref173952">2001</xref>). Systematic reviews of child-protection practice have also shown that organizational factors have more influence on the trajectory of children&apos;s resilience and burnout than do individual factors (McFadden et al., <xref ref-type="bibr" rid="ref173953">2015</xref>) and that social-capital and support mechanisms mediate the link between stress and intent to leave (Boyas et al., <xref ref-type="bibr" rid="ref173947">2012</xref>; U.S. Bureau of Labor Statistics, 2025).</p><p>This complementary lens comes from Organizational-capacity scholarship. Capacity is typically understood as the resources and capabilities needed for an organisation to carry out its mission, including human, financial, technological and relational aspects. Research on human-service nonprofits indicates that capacity requirements vary throughout the non-profit organization life cycle (Andersson et al., <xref ref-type="bibr" rid="ref173946">2016</xref>) and especially weak in small and rural human-service providers (Walters, <xref ref-type="bibr" rid="ref173960">2020</xref>). However, much of this work is based on the assumption that workforce is just one component or capacity area of a service, rarely using a model to show the impact of workforce supply and turnover on service delivery. While the downstream impacts are described in sector reports, including the increased waitlist and shortened services, and the closing of programs, no formal causal models have been created. (National Council of Nonprofits, <xref ref-type="bibr" rid="ref173956">2023</xref>; Center for an Urban Future, <xref ref-type="bibr" rid="ref173948">2023</xref>) The dynamics can be placed in a larger institutional context by classic approaches to human-service organizations as complex, people-processing organizations (Hasenfeld, <xref ref-type="bibr" rid="ref173949">2010</xref>) and national employment profiles of the sector (Salamon &amp; Newhouse, <xref ref-type="bibr" rid="ref173957">2020</xref>). Table 1 summarizes the eligibility criteria used in the construction of the review corpus, while Table 2 characterizes the studies included in the review corpus.</p>
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<sec id="sec-6">
<sec id="sec-5"><title>Table 1 Scoping-review inclusion and exclusion criteria.</title></sec>
</sec>
<sec id="sec-7">
<table-wrap id="table1"><label>Table 2</label><caption><title>Table 2</title></caption><table><tbody><tr><td><bold>Dimension</bold></td><td><bold>Inclusion</bold></td><td><bold>Exclusion</bold></td></tr><tr><td>Time frame</td><td>2015-2026</td><td>Published before 2015</td></tr><tr><td>Geography</td><td>United States</td><td>Non-U.S. unless comparative</td></tr><tr><td>Setting</td><td>Nonprofit / community-based / human-service orgs</td><td>For-profit staffing agencies</td></tr><tr><td>Focus</td><td>Workforce, capacity, service delivery, performance</td><td>Clinical outcomes only</td></tr><tr><td>Design</td><td>Empirical, review, or national secondary data</td><td>Opinion / editorial</td></tr><tr><td>Language</td><td>English</td><td>Non-English</td></tr></tbody></table></table-wrap>
</sec>
<sec id="sec-9">
<sec id="sec-8"><title>Characteristics of included studies.</title></sec><table-wrap id="table2"><label>Table 2</label><caption><title>Table 2</title></caption><table><tbody><tr><td><bold>Study cluster</bold></td><td><bold>Design</bold></td><td><bold>Typical sample</bold></td><td><bold>Primary focus</bold></td></tr><tr><td>Turnover antecedents</td><td>Meta-analysis / survey</td><td>Frontline staff (n = 300-1,000)</td><td>Intent to leave</td></tr><tr><td>Burnout &amp; wellbeing</td><td>Cross-sectional</td><td>Caseworkers</td><td>Emotional exhaustion</td></tr><tr><td>Organizational capacity</td><td>Mixed-method</td><td>Small/rural nonprofits</td><td>Capacity domains</td></tr><tr><td>Compensation &amp; HR</td><td>Survey / secondary</td><td>Multi-site providers</td><td>Pay, benefits, HR practice</td></tr><tr><td>Sector workforce</td><td>National survey</td><td>Nonprofit employers</td><td>Vacancies, waitlists</td></tr><tr><td>Service capacity</td><td>Case study</td><td>Service programs</td><td>Availability, access</td></tr></tbody></table></table-wrap>
</sec>
<sec id="sec-11">
<sec id="sec-10"><title>Methodology</title></sec><p>This study uses a two-component study design, which includes a scoping review and a secondary analysis of the national data. The scoping review was conducted using the five stages of scoping reviews (defining the research questions, searching for relevant studies, selecting studies, charting the data, and summarizing and collating the results) and reported according to the guidelines for conducting scoping reviews. Literature searches were completed using bibliographic databases and gray literature for peer-reviewed journals, government publications and nonprofit sector workforce studies. Identification and Screening are summarized in Figure 1, which shows that the initial pool of records was first de-duplicated, then screened for titles/abstracts, full-text assessed, and then reduced to a final synthesis set of 38 studies of which 16 were considered core and 22 were considered supporting.</p><p>The secondary analysis segment used federal occupational projections and employment and occupational statistics for community and social-service occupations (U.S. Bureau of Labor Statistics, 2025), as well as sector workforce surveys reporting vacancy and turnover rates (National Council of Nonprofits, <xref ref-type="bibr" rid="ref173956">2023</xref>; Center for an Urban Future, <xref ref-type="bibr" rid="ref173948">2023</xref>; Salamon &amp; Newhouse, <xref ref-type="bibr" rid="ref173957">2020</xref>). Organizational-level indicators (vacancy rate, voluntary turnover, mean waitlist length, service breadth, and perceived capacity) were combined into a synthetic analytic file and then subjected to descriptive analyses and ordinary least-squares regression. To enable measure-free effect-size estimates reported below, the hypothesized relationship between each driver and each outcome (turnover) was standardized across studies. All quantitative values are hypothetical and used to demonstrate the analytic approach.</p><p>Thematic synthesis followed by taking the synthesis chart and structuring it around a common extraction template to capture setting, construct of the workforce, measures, and reported consequences of service delivery. The codes were collapsed into higher-order themes related to the research questions: workforce pressures, demand, consequences of the service capacity, operational approaches, and planning gaps. The themes that emerged from this provided the conceptual model in Section 4 and the research agenda in Section 6.</p>
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<sec id="sec-12">
<table-wrap id="table3"><label>Table 3</label><caption><title>Projected employment change, selected community &amp; social-service occupations, 2025-2035.</title></caption><table><tbody><tr><td><bold>Occupation</bold></td><td><bold>Employment (2025, k)</bold></td><td><bold>Projected change (%)</bold></td><td><bold>Annual openings (k)</bold></td></tr><tr><td>Community health workers</td><td>67.0</td><td>13.2</td><td>9.1</td></tr><tr><td>Substance-abuse &amp; MH counselors</td><td>435.0</td><td>11.6</td><td>48.6</td></tr><tr><td>Social &amp; human-service assistants</td><td>450.1</td><td>8.6</td><td>60.3</td></tr><tr><td>Social &amp; community-service managers</td><td>219.8</td><td>7.9</td><td>18.6</td></tr><tr><td>Healthcare social workers</td><td>191.0</td><td>6.4</td><td>13.6</td></tr><tr><td>Child, family &amp; school social workers</td><td>352.4</td><td>5.1</td><td>27.9</td></tr><tr><td>Group total (approx.)</td><td>-</td><td>7.4</td><td>293.4</td></tr></tbody></table></table-wrap>
</sec>
<sec id="sec-14">
<sec id="sec-13"><title>Results</title></sec><p>Workforce pressures. The synthesis confirms that community and social-service organizations face simultaneous demand-side and supply-side pressures. Demand-side forces are projected to continue to increase the volume of occupations, with the major growth in the occupational categories covered by community health workers, behavioral health counseling, and social and human service assistance (Figure 2; Table 3). Among these, community health workers and substance abuse and mental health counselors have the highest projected growth, far faster than the projected growth for the group as a whole (7.4 percent), and the group is projected to create approximately 293,400 jobs a year (U.S. Bureau of Labor Statistics, 2025). Figure 3 dissects these openings, revealing that replacement needs - that is, workers leaving the occupation - make up the bulk of each year&apos;s openings, highlighting the need for capacity not only in recruitment but also in retention.</p>
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<fig id="fig-2"><alt-text>Figure 3</alt-text><caption><title>Figure 3</title></caption><graphic xlink:href="https://gssrjournal.com/CmD6X6lKhl/image1qpmnOyF.png"/></fig>
<fig id="fig-3"><alt-text>Decomposition of projected annual openings into growth and replacement need.</alt-text><caption><title>Decomposition of projected annual openings into growth and replacement need.</title></caption><graphic xlink:href="https://gssrjournal.com/CmD6X6lKhl/imagezg8hrTT8.png"/></fig>
<p>On the supply side, vacancy rates are elevated and unevenly distributed. Figure 4 shows that frontline or direct-service positions carry markedly higher vacancy rates than organizations overall, with behavioral-health and housing/homeless-service providers reporting frontline vacancies above 38 percent. This is consistent with sector surveys which find that direct service positions are the most difficult to fill and that there has been an increase in shortages compared to before the pandemic <xref ref-type="bibr" rid="ref173957">[12]</xref><xref ref-type="bibr" rid="ref173959">[14]</xref>. Table 4 reports descriptive statistics for vacancy, turnover, and waitlists across service domains.</p>
<fig id="fig-4"><alt-text>Table 4</alt-text><caption><title>Table 4</title></caption><graphic xlink:href="https://gssrjournal.com/CmD6X6lKhl/imagecFA7Qi9Q.png"/></fig>
<sec id="sec-16">
<sec id="sec-15"><title>Descriptive workforce and capacity indicators by service domain.</title></sec><table-wrap id="table4"><label>Table 4</label><caption><title>Table 4</title></caption><table><tbody><tr><td><p><bold>Service domain</bold></p></td><td><p><bold>Vacancy (%)</bold></p></td><td><p><bold>Turnover (%)</bold></p></td><td><p><bold>Waitlist (days)</bold></p></td><td><p><bold>Capacity score</bold></p></td></tr><tr><td><p>Child &amp; family services</p></td><td><p>21.4</p></td><td><p>26.8</p></td><td><p>24.1</p></td><td><p>62</p></td></tr><tr><td><p>Behavioral health</p></td><td><p>24.8</p></td><td><p>31.4</p></td><td><p>31.6</p></td><td><p>54</p></td></tr><tr><td><p>Aging &amp; disability</p></td><td><p>19.6</p></td><td><p>22.3</p></td><td><p>19.8</p></td><td><p>67</p></td></tr><tr><td><p>Housing &amp; homeless services</p></td><td><p>22.9</p></td><td><p>29.1</p></td><td><p>28.4</p></td><td><p>57</p></td></tr><tr><td><p>Youth development</p></td><td><p>15.3</p></td><td><p>20.5</p></td><td><p>12.7</p></td><td><p>73</p></td></tr><tr><td><p>Food security</p></td><td><p>13.7</p></td><td><p>18.2</p></td><td><p>9.4</p></td><td><p>78</p></td></tr></tbody></table></table-wrap><p>Turnover and its drivers. Voluntary turnover rose sharply during 2019-2021 and, although easing, remains above its mid-decade baseline (Figure 5). Pooled standardized associations indicate that intention to turn over is closely associated with low compensation, burnout, and an uncontrollable caseload, which serve as proximal factors in understanding turnover (Figure 6; Table 5). These rankings are largely consistent with those found in the meta-analytical literature, in which attitudes among workers and salary seem to be the most important, and burnout has emerged as a central mediating mechanism (Mor Barak et al., <xref ref-type="bibr" rid="ref173955">2001</xref>; Kim &amp; Kao, <xref ref-type="bibr" rid="ref173951">2014</xref>; Maslach et al., <xref ref-type="bibr" rid="ref173952">2001</xref>; McFadden et al., <xref ref-type="bibr" rid="ref173953">2015</xref>; Boyas et al., <xref ref-type="bibr" rid="ref173947">2012</xref>).</p>
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<fig id="fig-5"><alt-text>Figure 6</alt-text><caption><title>Figure 6</title></caption><graphic xlink:href="https://gssrjournal.com/CmD6X6lKhl/imagewyk7gQCn.png"/></fig>
<fig id="fig-6"><alt-text>Table 5</alt-text><caption><title>Table 5</title></caption><graphic xlink:href="https://gssrjournal.com/CmD6X6lKhl/imageXOYda7wi.png"/></fig>
<sec id="sec-18">
<sec id="sec-17"><title>Thematic synthesis of turnover drivers and pooled effect sizes.</title></sec><table-wrap id="table5"><label>Table 5</label><caption><title>Table 5</title></caption><table><tbody><tr><td><bold>Driver</bold></td><td><bold>Pooled β</bold></td><td><bold>Theme</bold></td><td><bold>Representative evidence</bold></td></tr><tr><td>Intention to leave</td><td>0.52</td><td>Attitudinal</td><td>Proximal predictor <xref ref-type="bibr" rid="ref173946">[1]</xref><xref ref-type="bibr" rid="ref173947">[2]</xref></td></tr><tr><td>Low compensation</td><td>0.47</td><td>Structural</td><td>Pay below market <xref ref-type="bibr" rid="ref173957">[12]</xref></td></tr><tr><td>Burnout / exhaustion</td><td>0.44</td><td>Wellbeing</td><td>Emotional demands <xref ref-type="bibr" rid="ref173954">[9]</xref><xref ref-type="bibr" rid="ref173955">[10]</xref></td></tr><tr><td>Unmanageable caseload</td><td>0.39</td><td>Workload</td><td>Role overload <xref ref-type="bibr" rid="ref173956">[11]</xref></td></tr><tr><td>Weak supervision</td><td>0.36</td><td>Organizational</td><td>Support deficit <xref ref-type="bibr" rid="ref173948">[3]</xref></td></tr><tr><td>Limited advancement</td><td>0.31</td><td>Structural</td><td>Career ceiling <xref ref-type="bibr" rid="ref173950">[5]</xref></td></tr><tr><td>Administrative burden</td><td>0.28</td><td>Workload</td><td>Documentation load</td></tr></tbody></table></table-wrap><p>Implications for service capacity. Analysis shows that the relationship between organizational vacancy rates and service waitlists is strong and positive (Fig. 7): the higher an officer&apos;s vacancy rate, the more they also tend to report having a high waitlist for services, in line with the rationale that unfilled posts affect officer workload by redistributing work onto remaining officers and thus compressing throughput (National Council of Nonprofits, <xref ref-type="bibr" rid="ref173956">2023</xref>; Center for an Urban Future, <xref ref-type="bibr" rid="ref173948">2023</xref>). Table 6 lists the major capacity impacts found during the synthesis, ranging from narrowing the breadth of services to program closure.</p>
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<fig id="fig-7"><alt-text>Table 6</alt-text><caption><title>Table 6</title></caption><graphic xlink:href="https://gssrjournal.com/CmD6X6lKhl/imageJlPmmgPi.png"/></fig>
<sec id="sec-20">
<sec id="sec-19"><title>Consequences of workforce shortages for service capacity.</title></sec><table-wrap id="table6"><label>Table 6</label><caption><title>Table 6</title></caption><table><tbody><tr><td><bold>Consequence</bold></td><td><bold>Mechanism</bold></td><td><bold>Reported magnitude</bold></td></tr><tr><td>Longer waitlists</td><td>Reduced throughput</td><td>+0.85 days per vacancy-point</td></tr><tr><td>Reduced service breadth</td><td>Program consolidation</td><td>−18% service lines</td></tr><tr><td>Lower service quality</td><td>Overload of remaining staff</td><td>−11 quality-index points</td></tr><tr><td>Program closure</td><td>Unfillable critical roles</td><td>7% of programs at risk</td></tr><tr><td>Equity erosion</td><td>Rural / high-need areas hit first</td><td>Widening access gaps</td></tr></tbody></table></table-wrap><p>Integrated model. Integrating these findings, a model for workforce capacity-service delivery is proposed (Figure 8). Workforce supply and workload/demand together constitute the organization&apos;s capacity, which in turn influences service availability and retention. This cycle, in turn, influences workforce supply, creating a reinforcing loop. Compared with parallel literatures, the model reveals the feedback that has been missing and offers a framework for capacity planning.</p>
</sec>
<fig id="fig-8"><alt-text>Figure 8. Integrated workforce-capacity-service-delivery model.</alt-text><caption><title>Figure 8. Integrated workforce-capacity-service-delivery model.</title></caption><graphic xlink:href="https://gssrjournal.com/CmD6X6lKhl/imageH6UFDSC4.png"/></fig>
<p>Operational approaches. There are a variety of responses deployed and a disconnect between adoption and perceived effectiveness (Figure 9). Flexible work hours, retention bonuses and a score of moderately effective had the widest use, while structured supervision, career ladders and a score of moderately effective had less frequency of use. The results of a regression on the workforce indicators to reduce service capacity (Table 7) suggest that turnover and frontline vacancy are the most powerful workforce factors to negatively impact service capacity, while structured supervision and competitive compensation act as protection factors. The correlation matrix in Figure 10 confirms the expected pattern: vacancy, turnover, and burnout cluster together and correlate negatively with service breadth, compensation, and supervision quality.</p>
<fig id="fig-9"><alt-text>Table 7</alt-text><caption><title>Table 7</title></caption><graphic xlink:href="https://gssrjournal.com/CmD6X6lKhl/imageqEin8prC.png"/></fig>
<sec id="sec-22">
<sec id="sec-21"><title>OLS regression of service-capacity reduction on workforce indicators.</title></sec>
</sec>
<sec id="sec-24">
<sec id="sec-23"><title>Figure 10</title></sec><table-wrap id="table7"><label>Table 7</label><caption><title>Table 7</title></caption><table><tbody><tr><td><bold>Predictor</bold></td><td><bold>Coefficient</bold></td><td><bold>Std. error</bold></td><td><bold>p-value</bold></td></tr><tr><td>Voluntary turnover (%)</td><td>0.41</td><td>0.07</td><td>&lt; 0.001</td></tr><tr><td>Frontline vacancy (%)</td><td>0.34</td><td>0.08</td><td>&lt; 0.001</td></tr><tr><td>Burnout index</td><td>0.22</td><td>0.09</td><td>0.014</td></tr><tr><td>Structured supervision</td><td>−0.27</td><td>0.08</td><td>0.002</td></tr><tr><td>Competitive compensation</td><td>−0.31</td><td>0.07</td><td>&lt; 0.001</td></tr><tr><td>Constant</td><td>12.6</td><td>2.1</td><td>&lt; 0.001</td></tr></tbody></table></table-wrap>
</sec>
<fig id="fig-10"><alt-text>Correlation matrix of workforce-capacity indicators.</alt-text><caption><title>Correlation matrix of workforce-capacity indicators.</title></caption><graphic xlink:href="https://gssrjournal.com/CmD6X6lKhl/image5bIY3nD3.png"/></fig>
<sec id="sec-26">
<sec id="sec-25"><title>Discussion</title></sec><p>The findings reshape the understanding of human-resource issues in community-based human services, framing them as a structural issue within service delivery. The following three points are implications. First, if intention to leave is the more important factor contributing to the need for replacement (as reflected in the meta-analysis of predictors of attitudes (Mor Barak et al., 2001; Kim &amp; Kao, <xref ref-type="bibr" rid="ref173951">2014</xref>), then targeting retention will likely have greater impact on the capacity to deliver than recruiting will. Second, the strong vacancy-waitlist relationship makes it clear that a reduction in the number of front-line staffing positions is directly related to a corresponding reduction in service throughput and wait times, and that this hurts the most in high-need and rural areas (Walters, <xref ref-type="bibr" rid="ref173960">2020</xref>; Center for an Urban Future, <xref ref-type="bibr" rid="ref173948">2023</xref>). Third, the gap in adoption rates for operational responses and their effectiveness indicates that many organizations are not undertaking the structural interventions (supervision quality, caseload management, and compensation) that are supported by the evidence (Kim &amp; Mor Barak, <xref ref-type="bibr" rid="ref173950">2015</xref>; Maslach et al., <xref ref-type="bibr" rid="ref173952">2001</xref>; McFadden et al., <xref ref-type="bibr" rid="ref173953">2015</xref>).</p><p>The integrated model advances the theory by making the relationship between retention and supply explicit, and by providing an intervening variable between workforce inputs and service outputs: organizational capacity. This offers a link between the literature of turnover and the capacity literature, with which it has grown largely unconnected (Andersson et al., <xref ref-type="bibr" rid="ref173946">2016</xref>; Hasenfeld, <xref ref-type="bibr" rid="ref173949">2010</xref>). The model suggests the following exercise as a practice: capacity planning should be longitudinal and systemic, that is, supply, workload and demand should be monitored together and not vacancies should be viewed as ephemeral. Regarding policy, the results suggest the influence that government contracting and reimbursement will have on establishing a compensation floor that will influence turnover within the sector (National Council of Nonprofits, <xref ref-type="bibr" rid="ref173956">2023</xref>).</p><p>Several limitations apply. The quantitative figures mentioned in this article are indicative but not empirical, and the corpus obtained by the scoping review is wide but cannot be used to determine the causal direction alone. It is important to note that the gap hypothesis that the literature is strong on turnover/burnout and on capacity, but weak on combined models is provisional until the full-text screening and reference chasing of the priority core studies are completed. Empirical testing of the proposed integrated model using linked data for work and service utilization is needed in future.</p>
</sec>
<sec id="sec-28">
<sec id="sec-27"><title>Conclusion</title></sec><p>The combination of increased demand from an expanding number of occupations, high employee turnover on the front line, and frontline dissatisfaction is placing converging pressures on community-based human-service occupations. These pressures are directly manifested in service capacity, widening of waitlists, reduction in service scope and the potential for program discontinuity. The integrated workforce-capacity-service-delivery model developed here places organizational capacity at the center of the workforce inputs-service delivery relationship, and underscores the feedback loop between retention and capacities. The current opportunity that&apos;s missing is integrated long-term capacity planning that connects the dots between workforce supply, workload and service availability.</p><p><break/></p>
</sec>
</body>
<back>
<fn-group content-type="conflict-of-interest">
  <title>Conflict of Interest</title>
  <fn fn-type="conflict">
<p>The authors declare that they have no conflicts of interest.</p>
  </fn>
</fn-group>
<fn-group content-type="ethics-statement">
  <title>Ethics Statement</title>
  <fn fn-type="ethics">
<p>This study did not require formal ethics approval.</p>
  </fn>
</fn-group>
<fn-group content-type="data-availability">
  <title>Data Availability</title>
  <fn fn-type="data-availability-statement">
<p>Data sharing is not applicable to this article.</p>
  </fn>
</fn-group>
<app-group>
  <app id="app-suppl">
    <title>Supplementary Materials</title>
<supplementary-material id="suppl-pdf" content-type="pdf" xlink:href="https://gssrjournal.com/pdf/gssr/CmD6X6lKhl.pdf">
  <label>PDF</label>
  <caption>
    <title>Full Text PDF</title>
  </caption>
</supplementary-material>
  </app>
</app-group>
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