Occupational Therapy Professional Identity: Learning From the <i>Muriel Driver Memorial</i> Lectures
Bibliographic record
Abstract
Background. Given the well-documented professional identity challenges experienced by occupational therapists, reinforcing the profession's identity (collective and individual) is crucial for navigating changing environments and optimizing its contribution. The Muriel Driver Memorial Lectureship is an important component of the collective identity of the profession in Canada. Purpose . A professional identity lens was used to trace the evolution of the profession's collective identity in Canada through this lectureship. Method. Using sociological professional identity theory, a documentary longitudinal analysis was conducted on the 43 published lectureship articles (1975–2023), identifying key messages, values, knowledge, and practices. Findings. Eight main themes were identified: professional identity, epistemology, axiology, change and leadership, contribution, history, quality, and technology. The analysis revealed an evolving common base of values (occupation, client-centred, social justice) and knowledge (occupation-centred). Persistent challenges included defining theoretical foundations, resisting the biomedical model, and realizing the social vision in practice. The lectures highlighted occupational therapists’ evolving roles and ability to contribute to and lead change. Conclusion. The lectures provide insights into the evolution of occupational therapy's collective identity in Canada. Despite ongoing challenges, the contemporary context appears to be increasingly favourable for occupational therapists to practise consistent with the collective identity trends identified.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".