Family physicians’ perspectives on advising patients on an obstetric delivery location
Bibliographic record
Abstract
OBJECTIVE: To determine what factors individual physicians in a remote northern Saskatchewan community consider when advising patients on an obstetric delivery location. DESIGN: Semistructured interviews. SETTING: La Ronge, a remote northern Saskatchewan community. PARTICIPANTS: Eleven family physicians providing full-scope care in a remote medical clinic. METHODS: Each physician at the only medical clinic in the area was sent an email in February 2017 inviting them to be involved in the study. Interviews were conducted between February and April 2017 and were audiorecorded and manually transcribed. The transcribed interviews were returned to participants for their review and additional input. Saturation was reached after 9 interviews, with 2 further interviews being conducted after saturation for a total of 11 interviews. Inductive thematic analysis was undertaken following the interviews. MAIN FINDINGS: Patient choice was an important factor in the discussion about delivery location. Themes that evolved from physician consideration on delivery location included benefits of local delivery, local resources, selection of low-risk pregnancies, physician and nursing skills, and patient choice. CONCLUSION: Family physicians should provide patients with all potential options for delivery location and support the decision made with the patient.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".