A Sensibility Assessment of the Job Demands and Accommodation Planning Tool (JDAPT): A Tool to Help Workers with an Episodic Disability Plan Workplace Support
Bibliographic record
Abstract
Purpose Sensibility refers to a tool's comprehensiveness, understandability, relevance, feasibility, and length. It is used in the early development phase to begin assessing a new tool or intervention. This study examined the sensibility of the job demands and accommodation planning tool (JDAPT). The JDAPT identifies job demands related to physical, cognitive, interpersonal, and working conditions to better target strategies for workplace supports and accommodations aimed at assisting individuals with chronic health conditions. Methods Workers with a chronic health condition and workplace representatives were recruited from health charities, workplaces, and newsletters using convenience sampling. Cognitive interviews assessed the JDAPT's sensibility. A 70% endorsement rate was the minimum level of acceptability for sensibility concepts. A short screening tool also was administered, and answers compared to the complete JDAPT. Results Participants were 46 workers and 23 organizational representatives (n = 69). Endorsements highly exceeded the 70% cut-off for understandability, relevance, and length. Congruence between screening questions and the complete JDAPT suggested both workers and organizational representatives overlooked job demands when completing the screener. Participants provided additional examples and three new items to improve comprehensiveness. The JDAPT was rated highly relevant and useful, although not always easy to complete for someone with an episodic condition. Conclusions This study highlights the need for tools that facilitate accommodations for workers with episodic disabilities and provides early evidence for the sensibility of the JDAPT.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".