A Method to Assess Work Task Preferences
Bibliographic record
Abstract
Persons with intellectual disability may encounter difficulties in making choices and expressing preferences because of restricted communication skills or a tendency to acquiesce. In addition, many studies provide evidence that these persons have less opportunity to make choices and express their preferences. The aim of this study was to conduct a field test of an innovative method to assess vocational preferences using choice and task completion observations. Sixteen educators were trained to use this method. They were recruited through local developmental disability agencies specializing in services for persons with intellectual disability in the Province of Quebec (Canada). Nineteen persons with intellectual disability were assessed. Occurrences of four types of behaviors (choice, refusal, positive emotional and off-task behaviors), as well as length of time spent working on the task, were computed to determine levels of preferences. Interviews were conducted with the educators to collect their perceptions regarding the effectiveness and usefulness of the method as a measure of its value in use. Results suggest that this method is useful to assess vocational preferences with persons with intellectual disability. Interviews conducted with educators reveal a high satisfaction with the method. Vocational preferences assessment should rely on frequency of choices, as other behaviors previously considered as expressing preferences are not reliable. This study also provides further evidence that proxy opinions may differ from one's actual preferences.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.013 | 0.004 |
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".