Working the frontlines: A case study of job dissatisfaction among paid employees in the John Howard Society
Bibliographic record
Abstract
The experience of job dissatisfaction in Canada’s non-profit frontline workforce has led to problematic turnover rates, employee burnout/fatigue, and a reduced quality of service. The non-profit sector provides valuable services to vulnerable populations, such as the elderly, at-risk youth, people re-integrating back into society from prison, the disabled, those struggling with mental health issues, abused children, and other marginalized groups of people. Our governments cannot always provide services for these populations directly, and it is often the non-profit sector that reaches out and assists. This paper argues that frontline service quality is a public issue, and one way to address service quality is to focus on the overall wellness of the frontline work force and find ways to strengthen teams, build trust, loyalty, job commitment, intrinsic worth, and improve workplace health. The John Howard Society (JHS) was utilized as a case study to illustrate the major features of job dissatisfaction, specifically in Community Residential Facilities (CRFs) – halfway houses. An extensive literature review, frontline employee surveys, and interviews with JHS upper management personnel informed the policy analysis and recommendations. Several options are researched and analyzed, including: (1) workplace mentoring, (2) employee wellness, (3) team building, and (4) hiring a Director of HR. Each policy option is evaluated on its cost-effectiveness, equity, affordability, and impacts to overall job satisfaction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".