Choice‐Making in Vocational Activities Planning: Recommendations from Job Coaches
Bibliographic record
Abstract
Abstract Choice in the job seeking process may lead to increased satisfaction with the chosen job, and improve attention, performance, and motivation. Consequently, providing opportunities to express choices and interests while planning vocational activities is a key factor in achieving employment outcomes. Despite their commitment to promoting choice‐making, service providers encounter important barriers to understanding the vocational interests of persons with intellectual disabilities who may have difficulty expressing their choices verbally. Methods of recording choices expressed through nonverbal means of communication are therefore needed. Such a method was designed and field‐tested. Interviews were conducted with participating job coaches to assess its practical value and provide recommendations pertinent to its implementation and dissemination. This step is crucial to the knowledge‐to‐action process since it tailors research findings to make them meaningful to daily practice. The authors present results relevant to improving choice‐making opportunities in the job seeking and planning process of persons with intellectual disabilities. The results demonstrate the need for training to enable support staff to embed choice‐making opportunities in the daily life of persons with intellectual disabilities.
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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 | 0.043 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".