Establishing a distributed campus: making sense of disruptions to a doctor community
Bibliographic record
Abstract
CONTEXT: In August 2004, the Northern Medical Program (NMP), a distributed campus of the Faculty of Medicine at the University of British Columbia, Canada, admitted its first students. Situated at the University of Northern British Columbia in Prince George, the NMP created new opportunities, challenges, stresses and changes for the approximately 180 local specialists and family doctors. This study examines the initial impacts of the NMP on doctors practising in its host community. METHODS: Qualitative interview methods were used. A purposive sample was drawn from: (i) doctors who had involvement with the NMP, and (ii) doctors who were not involved with the NMP. Data were collected from May to September 2007 using a semi-structured interview guide. Interviews were audiotaped, transcribed and checked by participants. Analysis involved identifying, coding and categorising key emergent themes until saturation. RESULTS: Prior to the implementation of the NMP, doctors in Prince George had formed cohesive networks, in the face of adverse conditions, that functioned effectively as a form of social capital. The introduction of new doctors and resources through the NMP disrupted this sense of community cohesiveness. Over time, however, the NMP has created new mechanisms by which doctors interact and develop partnerships. DISCUSSION: The study confirms the value of a social capital framework for understanding a medical community's adaptation to change. At this early point, it appears the NMP has created new mechanisms by which doctors can interact and develop the partnerships and relationships necessary to renew a sense of community cohesion.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.040 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.005 | 0.008 |
| 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".