Fidelity of Occupational Performance Coaching: Importance of Accuracy in Intervention Identification
Bibliographic record
Abstract
Establishing fidelity processes and measures is an important step in the development of interventions. Accurate referencing, naming of interventions and robust discussion of deviations from the theories, methods or terminology underpinning interventions support the fidelity of future applications of interventions in research and clinical settings. This commentary clarifies the establishment of fidelity for Occupational Performance Coaching (OPC) in relation to a recent article on this topic by Dunn and colleagues. Fundamental flaws in the referencing, labelling, theoretical underpinnings and methods inaccurately described as OPC are outlined. Guidance in establishing fidelity for future applications of OPC is provided.
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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 | 0.275 | 0.670 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.001 | 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".