Bibliographic record
Abstract
Purpose – There is a paucity of research that examines how diagnostic decisions are made by psychiatrists. Moreover, previous work in the area tends to be grounded in labeling theory, which highlights the conflict-based nature of diagnosis. The goal of this research is to examine the utility and benefits of diagnosis to psychiatrists' everyday work.Methodology – Using institutional ethnography (IE), I undertook a small-scale interview-based study that documented the diagnostic processes of three psychiatrists in Calgary, Alberta, Canada. The IE-based goals of the study were to: (1) identify what texts were employed during the diagnostic process, (2) map sequences of action and text that coordinated psychiatric decision-making, and (3) theorize the utility of diagnosis for the everyday work of psychiatrists.Findings – The analysis demonstrates how diagnosis can be understood as a valuable work process that produces a standardized diagnostic story in order to bring an individual's experiences of distress into relation with psychiatrists' daily practices, and institutional discourses more generally.Limitations – Although IE-based research does not depend on large sample sizes for analytic accuracy, results from the current study need to be replicated because of the limited number of interview participants and to examine whether the diagnostic process is generalizable to other settings.Social implications – This research challenges the idea that standardization through diagnosis is a negative process and highlights the value of diagnostic decision-making in the daily work of psychiatrists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".