City mouse, country mouse: a mixed-methods evaluation of perceived communication barriers between rural family physicians and urban consultants in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
OBJECTIVES: To examine perceived communication barriers between urban consultants and rural family physicians practising routine and emergency care in remote subarctic Newfoundland and Labrador (NL). DESIGN: This study used a mixed-methods design. Quantitative and qualitative data were collected through exploratory surveys, comprised of closed and open-ended questions. The quantitative data was analysed using comparative statistical analyses, and a thematic analysis was applied to the qualitative data. PARTICIPANTS: 52 self-identified rural family physicians and 23 urban consultants were recruited via email. Rural participants were also recruited at the Family Medicine Rural Preceptor meetings in St John's, NL. SETTING: Rural family physicians and urban consultants in NL completed a survey assessing perceived barriers to effective communication. RESULTS: Data confirmed that both groups perceived communication difficulties with one another; with 23.1% rural and 27.8% urban, rating the difficulties as frequent (p=0.935); 71.2% rural and 72.2% urban as sometimes (p=0.825); 5.8% rural and 0% urban acknowledged never perceiving difficulties (p=0.714). Overall, 87.1% of participants indicated that perceived communication difficulties impacted patient care. Primary trends that emerged as perceived barriers for rural physicians were time constraints and misunderstanding of site limitations. Urban consultants' perceived barriers were inadequate patient information and lack of native language skills. CONCLUSIONS: Barriers to effective communication are perceived between rural family physicians and urban consultants in NL.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".