How and Why : A Process Evaluation Proposal to Assess the Development Phase of Ergonomic Interventions
Bibliographic record
Abstract
Ergonomic interventions assessed by way of experimental methods appear to be over-simplified when they are limited to a standardized solution for a large number of workers. These interventions differ greatly from interventions provided by ergonomists out in the field who carry out a complex, participatory, change process closely adapted to an organization’s context. In such complex interventions, ergonomists carry out numerous actions before specific work modifications are implemented, but these actions are almost never mentioned in evaluation studies. The goal of this article is to present the methodological framework of a process evaluation focussing on the development phase of complex ergonomic interventions, the development phase occurring prior to the implementation of work modifications. The collection of quantitative and qualitative data in real time through a logbook, document analysis, and semi-structured interviews is proposed. This process evaluation model should provide knowledge of the actions that led to changes in specific contexts and that may represent the transferable aspect of the intervention to future interventions carried out in similar contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.272 | 0.229 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| 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".