A tale of two cultures: specialists and generalists sharing the load.
Bibliographic record
Abstract
OBJECTIVE: To understand what contributes to good collaborative physician working relationships through identifying the factors that affect working relationships between generalist physicians and specialists. DESIGN: Qualitative study using in-depth interviews. SETTING: University and community hospital inpatient family practice settings in Edmonton, Alta. PARTICIPANTS: Eleven physicians from various specialties who graduated between 1977 and 2001. METHODS: A grounded-theory approach, including constant comparison and creation of memorandums, helped to conceptualize the main concern and generated a framework for how the main concern was being resolved. A semistructured interview guide was developed and individual, in-depth interviews were audiotaped. Purposeful and theoretical sampling techniques were used. Three researchers participated in the analysis. MAIN FINDINGS: The findings suggested that when generalist physicians perceived that work had been imposed on them by specialists without negotiation, they felt overwhelmed by the workload. Differing priorities determined whether physicians were left holding the bag or sharing the load. In a system that valued technology and specialized knowledge and skills, the specialists were better able to control resources, set boundaries, and influence learners. This precipitated a culture of protecting valuable specialty resources, increasing physician isolation, and generalists feeling that they were left holding the bag. In order to reverse this cycle, it was important for physicians to develop good working relationships based on accessibility to needed expertise and tests, with negotiated agreements on how to share resources; mutual empowerment, including negotiation of roles and responsibilities to develop flexible relationships with a clear understanding of roles; and concern for fairness by sharing the load. CONCLUSION: Medical systems that value technology and focused interests might lead to someone being left holding the bag, contributing to generalists feeling overwhelmed and isolated within the system. A comprehensive system that values relationships might help to resolve issues created through perceived inequities in workload, disempowerment, and lack of understanding of roles.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".