Development and Content Validation of the Adaptation Process in Academia Questionnaire for Occupational Therapy Educators
Bibliographic record
Abstract
Objective. The process of adaptation in academia can best be understood and measured using valid and reliable tools. To understand how occupational therapy educators adapt to academic roles and how they use adaptation to build academic careers, the Adaptation Process in Academia Questionnaire (APA-Q) was developed. The APA-Q is a 199-item tool with four sections: academic experiences (104 items); contexts (16 items); adapting responses (13 items); and adaptation outcomes (66 items). This study described the development and the process of determining the content validity of the APA-Q.Method. We conducted an extensive review of literature and the available faculty instruments in developing the APA-Q items. Six content experts were recruited to rate the 199-item and scale relevance of the instrument. Qualitative feedback were provided from open-ended questions. Item and scale content validity indices (I-CVI/S-CVI) were calculated. CVI and qualitative assessment informed questionnaire revisions. Results. Content experts rated 161 of the items (81%) to be highly relevant. The I-CVI of 30 items was acceptable (0.83). Eight items were rated irrelevant (0.5-0.66). S-CVI was excellent (0.97). In terms of constructs, experts agreed on the relevance of items (>0.80): academic experiences (99 or 95%); contexts (16 or 100%); adapting responses (12 or 92%); and adaptation outcomes (63 or 95%). Qualitative assessment indicated a lack of clarity in some items and instructions, redundancy in some of the items, the use of jargon, and missing items. Based on I-CVI and qualitative assessment, 12 items were deleted, 13 items were revised, and 10 items were added. Conclusion. Context experts deemed the APA-Q to be relevant. Further establishment of its construct validity and reliability is warranted.
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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 | 0.052 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".