Understanding and building upon effort to return to work for people with long-term disability and job loss
Bibliographic record
Abstract
BACKGROUND: Effort is a concept that underlies programs assisting people with work disability to re-enter the labour force. During re-entry, attention is paid to the effort invested by the worker with an injury. However, for those with chronic work disability, the motivation to return to work (RTW) may be questioned by benefit service providers and healthcare professionals. OBJECTIVE: The objective of this paper is to describe the efforts made by people with long term work-disability to regain a foothold on the labour market. METHODS: This phenomenological study explored the meaning of work for people with long-term work disability and job loss. Twenty-seven interviews were conducted with nine participants. A thematic analysis was completed of the collected data. RESULTS: A key finding of this study is the variety and degree of effort exerted by participants to regain employment, despite time away from the workplace and system barriers. Effort was exerted to retain pre-accident employment; to obtain new work following job loss; and, to remain in a new job. CONCLUSIONS: This study suggests that if the RTW effort of people with long-term work disability is not fully acknowledged or supported, this population will remain unemployed where their strengths as competent, experienced workers will continue to be wasted.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".