Understanding Rural Patients’ Perspectives on Patient Care Teams
Bibliographic record
Abstract
Introduction: With a critical primary care clinician shortage, team-based care (TBC) is under development to enhance access, especially in rural regions. However, there has been limited discussion about TBC from the patient's perspective. We explored rural patient preferences for composition of their current and ideal primary health care team. Methods: An anonymous online survey of rural residents was conducted within the Kootenay-Boundary region of British Columbia. The survey included demographic information and current and ideal health care team composition. Descriptive statistics were used. Results: Four hundred ninety individuals responded to the survey. Most respondents self-identified as female, were over age 40 years, White, and had a postsecondary school diploma. Those with a health care provider (n=362, 74%) included a primary care doctor (86.5%), alternative medicine practitioner (52.2%), allied health professional (35.4%), friends/family/support people (35.1%), specialist physician (30.1%) and nurse practitioner (18.5%) on their team. Ideal health care teams included a primary care physician (92.2%) followed by an alternative medicine practitioner (64.1%), allied health professionals (61.2%), specialist physician (59.8%), and nurse practitioner (54.8%). Almost half of all respondents (46.6%) chose five or more categories of team members and 43.7% chose 3-4 categories of members. Respondents (81.1%) were highly likely (49.6%) or likely (31.6%) to affiliate with a primary health care team clinic. Conclusion: Both current and ideal health care team membership included many types of caregivers, with most respondents including three or more team-member categories. As many people in rural BC are without primary care providers, incorporating alternative medical practitioners and allied health professions into clinical teams within multidisciplinary settings could improve health care access.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".