Devising work schedules for a collective: Favouring intergenerational collaboration among counsellors in a shelter for female victims of conjugal violence
Bibliographic record
Abstract
OBJECTIVE: The work activity of counsellors in shelters for female victims of conjugal violence is explored. The consortium of shelters requested the study because of complaints of worker stress, difficulties in management and high employee turnover. METHODS: This qualitative and participatory community study involved a team of specialists in ergonomics and social work from the Centre de recherche interdisciplinaire sur la biologie, la santé, la sociélté et l'environnement (CINBIOSE), brought together by the Community Outreach Service of Université du Québec à Montréal (UQAM). Presented here are the study findings pertaining to training. Twenty-two semi-structured interviews and 80 hours of observation of work and training were conducted with counsellors from two contrasting shelters. RESULTS: Observations revealed an intense collaborative activity involving communication by many means. Nonetheless, young counsellors interviewed complained of having few opportunities to develop their counselling skills because they were isolated on evening, night and weekend shifts. In collaboration with the ergonomists, one shelter experimented with new ways of devising the work schedule to favour learning and training. CONCLUSION: By transforming the training mechanism, job status and work schedules, the shelter made the conditions more conducive to the development of counsellors' skills and health, while eliminating turnover for at least the two following years.
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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 | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".