Expectations and needs of socially vulnerable patients for navigational support of primary health care services
Bibliographic record
Abstract
BACKGROUND: Primary healthcare is the main entry to the health care system for most of the population. In 2008, it was estimated that about 26% of the population in Quebec (Canada) did not have a regular family physician. In early 2017, about 10 years after the introduction of a centralized waiting list for patients without a family physician, Québec had 25% of its population without a family physician and nearly 33% of these or 540,000, many of whom were socially vulnerable (SV), remained registered on the list. SV patients often have more health problems. They also face access inequities or may lack the skills needed to navigate a constantly evolving and complex healthcare system. Navigation interventions show promise for improving access to primary health care for SV patients. This study aimed to describe and understand the expectations and needs of SV patients. METHODS: A descriptive qualitative study rooted in a participatory study on navigation interventions implemented in Montérégie (Quebec) addressed to SV patients. Semi-structured individual face-to-face and telephone interviews were conducted with patients recruited in three primary health care clinics, some of whom received the navigation intervention. A thematic analysis was performed using NVivo 11 software. RESULTS: Sixteen patients living in socially deprived contexts agreed to participate in this qualitative study. Three main expectations and needs of patients for navigation interventions were identified: communication expectations (support to understand providers and to be understood by them, discuss about medical visit, and bridge the communication cap between patients and PHC providers); relational expectations regarding emotional or psychosocial support; and pragmatic expectations (information on available resources, information about the clinic, and physical support to navigate the health care system). CONCLUSIONS: Our study contributes to the literature by identifying expectations and needs specified to SV patients accessing primary health care services, that relate to navigation interventions. This information can be used by decision makers for navigation interventions design and inform health care organizational policies.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".