Causes and decision paths of employee turnover in the homeless service sector
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".