On Effect Assessment in Work Environment Interventions – A Literature Overview and Methodological Reflection
Bibliographic record
Abstract
This paper addresses a number of issues for work environment intervention (WEI) researchers in light of the mixed results reported in the literature. If researchers emphasise study quality over intervention quality, reviews that exclude case studies with high quality and multifactorial interventions may be vulnerable to ‘quality criteria selection bias’. Learning from ‘failed’ interventions is inhibited by both publication bias and reporting lengths that limit information on relevant contextual and implementation factors. The authors argue for the need to develop evaluation approaches consistent with the complexity of multifactorial WEIs that: a) are owned by and aimed at the whole organisation; and b) include intervention in early design stages where potential impact is highest. Context variety, complexity and instability in and around organisations suggest that attention might usefully shift from generalisable ‘proof of effectiveness’ to a more nuanced identification of intervention elements and the situations in which they are more likely to work as intended.
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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 | 0.314 | 0.414 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".