Influencing Factors on Rural Maternity Care Practice
Bibliographic record
Abstract
Background: Rural maternity care in Canada is in crisis, with many communities losing local services. This forces rural women and families to travel for care, leading to heightened stress, expenses, and adverse outcomes. Family physicians, often the primary providers of rural maternity care, are decreasing in numbers, exacerbating the problem. Without enough providers, rural communities struggle to offer safe and accessible maternity services, risking the health of expectant mothers and families. Objective: This research aims to gain a comprehensive understanding of the socio-ecological influences that shape the commitment of family physicians and residents to practice rural maternity care. Methods: A scoping review was conducted, and database searching occurred in Ovid Medline, Ovid Embase, Ovid Emcare, and Web of Science. Primary studies and literature reviews in English were included if they discussed family physicians' and residents' experiences and perspectives in practicing and training for rural maternity care. Articles were restricted to the past 30 years. Thematic analysis was applied to analyze the data, and results were reported in tabular format. Results: Influencing factors were categorized into themes and contextualized across the socio-ecological model: 1) individual factors (i.e. interests, attitudes, motivation, burnout, risk), 2) interpersonal factors (i.e. lifestyle, interprofessional relationships, mentors), 3) organizational factors (i.e. training and professional development, work environment and practice characteristics, resources, regulation and privileging), 4) community-level factors (i.e. practice setting and location, job availability, community context), and 5) systematic factors (healthcare system structure, public policy, legal and regulatory framework). Conclusion: The most salient influencing factors included challenges with Family Medicine residency training and role models, call schedule sustainability and interprofessional collaboration, as well as preserving clinical skills and financial stability with low procedural volume in rural communities. There is a need to implement evidence-based interventions targeting training, recruiting role models, interprofessional collaboration and call, and effective rural remuneration.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".