Twenty years of assessment in WORK: A narrative review
Bibliographic record
Abstract
INTRODUCTION: The aim of this review was to gain an understanding of the first 20 years of contributions to WORK within the assessment domain and to reflect on the perspectives underscoring this knowledge base. METHOD: A narrative review of assessment articles using the WORK ARTicle database was conducted. Assessment articles were searched using issues from 1990 to 2009. Descriptive data was analyzed to examine historical trends of the specific types and dimensions of articles, the regional location of the contributions, and the methodological accordance. A reflective process was used by an editorial board member of WORK to inductively interpret perspectives and contextual issues that underpinned the evolution of the assessment domain in WORK. RESULTS: Over half of $N=$ 108 of the articles on assessment in WORK focused on establishing or reporting reliability and validity of assessments used in clinical practice or evaluation research. The majority of the assessment articles were predominantly focused on the person. Contributions of articles were from 5 regions: North America, Europe, Australia, Asia and Africa. CONCLUSIONS: Assessment articles in WORK have contributed to the development of evidence to support assessment of the worker. These articles represent a knowledge base that emphasizes evidence-based assessments to evaluate what a person can and cannot do to participate in work. Efforts are needed to expand knowledge generation in assessment to include more evaluations on the workplace and occupation dimensions, and that also considers the worker in context.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".