Occupational Science Concepts Essential to Occupation-Based Practice: Development of Expert Consensus
Bibliographic record
Abstract
IMPORTANCE: What occupational science (OS) knowledge may be essential to occupational therapy practice has not been systematically explored. OBJECTIVE: To identify and gain expert consensus on OS concepts viewed as essential to occupational therapy practice. DESIGN: A complex, convergent mixed-methods Delphi design with an international panel of OS experts randomly assigned to two parallel groups. In Round 1, each group generated OS concepts; in Rounds 2 and 3, they rated the degree to which each concept was essential to occupational therapy. Data were analyzed separately for each group. A fourth round combined the two groups and used carefully merged concept definitions from both groups to validate consensus on essential concepts arising from the prior rounds. PARTICIPANTS: Fifty-two nominated experts from 22 countries who met a priori criteria participated in the 14-mo study. RESULTS: Of 62 experts invited, 52 (Group A = 24, Group B = 28) participated in the first round, and 42 (81%) completed the full-group final round. Eleven concepts met the consensus threshold (≥70%) established for the study. Additional analysis compared parallel- and full-group results to carefully discern conceptual similarities and differences, especially with near-consensus concepts. CONCLUSIONS AND RELEVANCE: Substantial expert agreement was established for several OS concepts viewed as essential, providing a basis for future studies to refine the concepts for occupational therapy education and practice. What This Article Adds: The results of this research provide a systematically derived preliminary basis for selecting OS content for occupational therapy educational programs and preliminary concepts for organizing OS knowledge germane to occupational therapy practice.
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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 | 0.223 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".