Access to primary health care: perspectives of primary care physicians and community stakeholders
Bibliographic record
Abstract
BACKGROUND: Action on the social determinants of health is important to strengthen primary health care and promote access among underserved populations. We report on findings from stakeholder consultations undertaken at one of the Canadian sites of the Innovative Models Promoting Access-to-Care Transformation (IMPACT) program, as part of the development of a best practice intervention to improve access to primary health care. The overarching objective of this qualitative study was to understand the processes, barriers, and facilitators to connect patients to health enabling community resources (HERs) to inform a patient navigation model situated in primary care. METHODS: Focus groups and interviews were conducted with primary care physicians, and community health and social service providers to understand their experiences in supporting patients in reaching HERs. Current gaps in access to primary health care and the potential of patient navigation were also explored. We applied Levesque et al., (2013) access framework to code the data and four themes emerged: (1) Approachability and Ability to Perceive, (2) Acceptability and Ability to Seek, (3) Availability and Accommodation, and Ability to Reach, and (4) Appropriateness. RESULTS: Determinants of access included patient and provider awareness of HERs, the nature of the patient-provider relationship, funding of HERs, integration of primary and community care services, and continuity of information. Participants' perspectives about the potential scope and role of a patient navigator provided valuable insight for the development of the Access to Resources in the Community (ARC) navigation model and how it could be embedded in a primary care setting. CONCLUSION: Additional consultation with key stakeholders in the health region is needed to gain a broader understanding of the challenges in caring for primary care patients with social barriers and how to support them in accessing community-based primary health care to inform the design of the ARC intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| 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".