Identifying Motivators for Retention of Service Workers in Youth Group Care Programs
Bibliographic record
Abstract
Residential group care programs in Calgary, Alberta, Canada face challenges in recruiting and retaining qualified human service workers. This workforce retention issue affects the quality of care provided to vulnerable children and youth, as well as the overall stability of residential group care. The purpose of this qualitative action research study was to explore why service workers in front-line jobs choose to stay in their positions in youth-serving residential group care programs, guided by Herzberg’s two-factor theory of motivation. Nine participants were recruited through purposeful sampling via social media and email, with the criterion that they had been employed at a residential group care program in Calgary, Alberta, for at least 1 year. Data were analyzed using thematic coding and inductive analysis. Key results included diversity of roles and employment length, fulfillment of purpose, commitment to youth and improving care systems, pursuit of professional growth, fulfillment from witnessing youth growth, satisfaction from improving professionally, reliance on supports, connection to people and the work, resilience to manage the demands of the work, suggested improvements to increase retention, and alignment to residential group care work. Recommendations included developing and prioritizing structured and ongoing relational supervision practices, creating a peer mentorship program and sector-wide network for new and emerging human service workers, and advocating for increasing compensation and equity across the sector. This study may lead to positive social change by enhancing services to children and youth in residential group care programs, thereby increasing the retention rates of human service workers.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".