The Job Demands and Accommodation Planning Tool (JDAPT): A Nine-Month Evaluation of Use, Changes in Self-efficacy, Presenteeism, and Absenteeism in Workers with Chronic and Episodic Disabilities
Bibliographic record
Abstract
PURPOSE: Enhancing workplace communication and support processes to enable individuals living with disabilities to sustain employment and return to work is a priority for workers, employers, and community stakeholders. The objective of this study was to evaluate a new resource that addresses support challenges, the Job Demands and Accommodation Planning Tool (JDAPT), and assess its use, relevance, and outcomes over a nine-month follow-up period. METHODS: Workers with physical and mental health/cognitive conditions causing limitations at work were recruited using purposive sampling. Online surveys were administered at baseline (prior to using the JDAPT), and at three and nine months post-baseline. Information was collected on demographics (e.g., age, gender) and work characteristics (e.g., job sector, organization size). Outcomes included assessing JDAPT use and relevance, and changes in self-efficacy, work productivity difficulties, employment concerns, difficulties with job demands, and absenteeism. RESULTS: Baseline participants were 269 workers (66% women; mean age 41 years) of whom 188 (69.9%) completed all three waves of data collection. Many workers reported using JDAPT strategies at and outside of work, and held positive perceptions of the tool's usability, relevance, and helpfulness. There were significant improvements (Time 1-2; Time 1-3) in self-efficacy, perceived work productivity, and absenteeism with moderate to large effect sizes in self-efficacy and productivity (0.46 to 0.78). Findings were consistent across gender, age, health condition, and work context variables. CONCLUSIONS: The JDAPT can enhance support provision and provide greater transparency and consistency to workplace disability practices, which is critical to creating more inclusive and accessible employment opportunities.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".