Bibliographic record
Abstract
Losing a job is one of life's most stressful crises. Alongwithlosingincome and the security associated with it, there are the losses of identity, the social network of co-workers, life purpose, and daily structure. One of the most difficult components of unemployment is that the day and week loom ahead with no prescribed benchmarks for waking, eating, going out, etc. Decision-making and time management skills are taxed as never before. When spouse and children understandably feel anxious, insecure, and angry, it often puts strains on relationships which add to the unemployed person's stress level. Many communities have programs which serve those who need basic life skills, or services which focus primarily on job search techniques. However, it became evident in the recession of the early 1980s that there were few services for those who had been in the workforce and had lost their jobs due to economic downturns and technological change. This was especially true for blue/white collar and middle management people, for unlike their executive and management counterparts, they rarely receive relocation counselling as part of their termination. Given that the unemployment rate across Canada is expected to hover in the nine percent range into the 1990s, it is important for those in the counselling field to understand the emotional effects of losing a job, in order to assist those who seek personal counselling in such a crisis. In an attempt to address this problem, a support group model, Handling Unemployment Groups (HUG) was developed by the Canadian Mental Health Association/Metro Toronto Branch, with a two-year funding grant from National Welfare Grants. The model was used with sixteen groups in the Toronto area, and the program evaluation demonstrated its effectiveness. Training in the model was conducted both provincially and nationally, with 185 professional counsellors. This report will discuss the objectives and philosophy of the H U G program, psycho-social issues related to unemployment, effective ways to assist unem-ployed people, and the program evaluation results.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".