A general framework for selecting work participation outcomes in intervention studies among persons with health problems: a concept paper
Bibliographic record
Abstract
Abstract Background Work participation is important for health and can be considered as engagement in a major area of life which is of significance for most people, but it can also be thought of as fulfilling or discharging a role. Currently, academic research lacks a comprehensive classification of work participation outcomes. The International Classification of Functioning is the foremost model in defining work functioning and its counterpart work disability, but it does not provide a critical (core) set of outcomes. Standardizing the definitions and nomenclature used in the research of work participation would ensure that the outcomes of studies are comparable, and practitioners and guideline developers can better decide what works best. As work participation is a broad umbrella term including outcome categories which need unambiguous differentiation, a framework needs to be developed first. Aim To propose a framework which can be used to develop a generic core outcome set for work participation. Methods First, we performed a systematic literature search on the concept of (work) participation, views on how to measure it, and on existing classifications for outcome measurements. Next, we derived criteria for the framework and proposed a framework based on the criteria. Last, we applied the framework to six case studies as a proof of concept. Results Our literature search provided 2106 hits and we selected 59 studies for full-text analysis. Based on the literature and the developed criteria we propose four overarching outcome categories: (1) initiating employment, (2) having employment, (3) increasing or maintaining productivity at work, and (4) return to employment. These categories appeared feasible in our proof-of-concept assessment with six different case studies. Conclusion We propose to use the framework for work participation outcomes to develop a core outcome set for intervention studies to improve work participation.
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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 | 0.327 | 0.247 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.055 | 0.024 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".