P-410 THE FEASIBILITY OF THE PROGRESSIVE GOAL ATTAINMENT PROGRAM IN THE NETHERLANDS ACCORDING TO WORKERS WITH HEALTH PROBLEMS
Bibliographic record
Abstract
Abstract Introduction The Progressive Goal Attainment Program (PGAP) is an intervention developed in Canada aimed at decreasing limiting cognitions and perceptions and increasing work participation. During PGAP, workers participate in a maximum of 10 one-hour sessions with a PGAP coach who encourages them to resume discontinued activities. The objective of this study was to evaluate the feasibility of PGAP in the Netherlands according to workers who were on part-time of full-time sick leave for at least three weeks due to health problems. Methods During a 30-minute structured online interview we asked ten workers about their experiences with PGAP. The questions we asked were based on the feasibility framework of Bowen, which describes eight areas of focus that should be addressed in feasibility studies: acceptability, demand, implementation, practicality, adaption, integration, expansion and limited-efficacy. Results The ten participants were overall very satisfied with PGAP and perceived it to be feasible. They had the idea that PGAP helped them to obtain more structure during the day and to participate in more activities, including work. Participants mentioned they would recommend PGAP to other workers. However, some participants mentioned that offering PGAP at an earlier stage of sick leave could possibly increase the positive effects of PGAP. Discussion The participants in this study perceived PGAP to be feasible. Future studies should indicate whether implementing PGAP is also (cost-)effective in increasing work participation. Conclusion The first workers who participated in PGAP in the Netherlands are satisfied about the program and perceive it to be feasible.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".