Reducing the impact of unemployment on health: revisiting the agenda for primary health care
Bibliographic record
Abstract
OBJECTIVE: To identify potentially effective strategies to be used in the primary health care (PHC) setting to prevent, detect and manage the health problems of unemployed people. DESIGN: A narrative review of articles on PHC-based interventions for unemployed people that were published during the period January 1985 to February 2009. RESULTS: Seven articles with a focus on improving the health of unemployed people through assessment, management and referral within PHC settings were identified. Four were based in Australia, and the others were from Canada and Europe. Most described interventions that incorporated strategies aimed at increasing general practitioners' awareness of the health problems of unemployed people and providing guidance on the management of these problems. One article included an evaluation of the impact of the intervention on health and social outcomes, but no impact was shown. CONCLUSIONS: There have been few formal scientific investigations into the effectiveness of PHC-based interventions for unemployed people. GPs and other community health workers have a central role in preventing, and providing early management of, the health problems of unemployed people, and supporting return to work. People who are unemployed have poorer physical and mental health than those who are employed. Research should move from describing these health problems to developing interventions that are subject to rigorous evaluation.
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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 | 0.056 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.022 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".