‘Return to the cave’ – exploring barriers to organizational learning in a psychiatric clinic
Bibliographic record
Abstract
This paper presents the findings of a five‐month graduate consulting project in a psychiatric clinic. The project was distinguished by the fact that I, the student consultant, had been a clinician in this system for several years, eventually leaving to practice in a larger university setting. Issues related to managing biases and maintaining the integrity of the consulting role will be an area of inquiry. Paradoxes that exist in the two systems will be highlighted in the context of bureaucratic obstacles and the ongoing stress of offering care in psychiatry. The paper will discuss factors that influence reflective practice using theoretical perspectives by Argyris and Schön, as well as exploring the conscious and unconscious forces that influence organizational learning. Using the metaphor of Plato's ‘Allegory of the Cave’ the paper will describe how the journey back to the system helped to inform my understanding of the various ingredients involved in transforming practices.
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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 | 0.026 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.030 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".