MétaCan
Menu
Back to cohort
Record W4379142895 · doi:10.1080/10530789.2023.2220526

Causes and decision paths of employee turnover in the homeless service sector

2023· article· en· W4379142895 on OpenAlexafffundabout
Joseph Voronov, Sean A. Kidd, Emmy Tiderington, John Ecker, Vicky Stergiopoulos, Nick Kerman

Bibliographic record

VenueJournal of Social Distress and the Homeless · 2023
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsYork UniversityUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health ResearchCentre for Addiction and Mental Health
KeywordsWorkforceTurnoverThematic analysisDismissalService providerBusinessService (business)HarmWork (physics)Qualitative researchPsychologyPublic relationsNursingMedicineMarketingSocial psychologyPolitical scienceEconomicsManagementSociology

Abstract

fetched live from OpenAlex

Community-based service providers working with people experiencing homelessness encounter many occupational challenges and high turnover rates are a challenge in the sector. This qualitative study examined the perceived causes and decision paths of turnover among service providers working with people experiencing homelessness in Canada. In-depth interviews were completed with 40 service providers working in homeless service, supportive housing, and harm reduction programs. Thematic analysis and a deductive-based matrix analysis were used to identify causes and decision paths of turnover, respectively. Primary causes of employee turnover included: (1) career advancement and growth; (2) incongruence between providers’ needs, values, and work position; (3) mental health deteriorations; (4) organizational stability and support issues; and (5) staff dismissal. Further, two contextual factors – low sectoral wages and a transitory work culture – shaped service providers’ occupational experiences and had indirect effects on turnover. Push and pull decisions were the primary paths to voluntary turnover among participants. Overall, the findings highlight that employee turnover often occurs voluntarily when service providers experience unmet occupational or work-interacting personal needs, or want to pursue new career-related opportunities. Practice and policy recommendations, including use of realistic job previews and establishment of workforce development strategies, are made to prevent turnover.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.368
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Social Distress and the HomelessSame topicEmployment and Welfare StudiesFrench-language works237,207