Exploring the interplay of discourses, jurisdictions and restratification in medicine and the implications for intraprofessional collaboration
Bibliographic record
Abstract
This study builds on calls to explore the tensions and dynamics of intraprofessional collaboration and boundary work. It reaches beyond the literature describing the micro-level strategies deployed by physician subgroups to establish legitimacy and defend jurisdictions in the face of health care re-organization. Specifically, it offers a view of how macrosocial imperatives shape intraprofessional boundaries, relations, and the possibilities for collaboration. Drawing on empirical data from a case study of intraprofessional collaboration in caring for patients with diabetes - a clinical context in which patients commonly receive care from family physicians (FPs) and specialist physicians (SPs) – Foucault's concept of governmentality and the sociology of the professions are employed to make visible the sociohistorical construction of intraprofessional collaboration within discourses of evidence-based medicine, and its implications for the [re-]negotiation of professional jurisdictions and restratification of the medical profession. This analysis contributes to the intraprofessional literature through two analytical moves. First, it outlines the discursive mechanisms through which the meso-level deployment of the referral-consultation process provides an arena for SPs to maintain and reinforce their position of influence at both the micro-level of daily clinical work and across broader health care delivery. Second, it provides an understanding of how the transmission of governmental rationality in diabetes occurs through the social relations between SPs and FPs, making the restratification of medicine possible without tension or conflict.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.059 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.028 | 0.126 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.007 | 0.007 |
| 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, unvalidatedLabeled directly by 3 models reading the full record.
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".