Patient, family member and caregiver engagement in shaping policy for primary health care teams in three Canadian Provinces
Bibliographic record
Abstract
INTRODUCTION: Improving health services integration through primary health care (PHC) teams for patients with chronic conditions is essential to address their complex health needs and facilitate better health outcomes. The objective of this study was to explore if and how patients, family members, and caregivers were engaged or wanted to be engaged in developing, implementing and evaluating health policies related to PHC teams. This patient-oriented research was carried out in three provinces across Canada: British Columbia, Alberta and Ontario. METHODS: A total of 29 semi-structured interviews with patients were conducted across the three provinces and data were analysed using thematic analysis. RESULTS: Three key themes were identified: motivation for policy engagement, experiences with policy engagement and barriers to engagement in policy. The majority of participants in the study wanted to be engaged in policy processes and advocate for integrated care through PHC teams. Barriers to patient engagement in policy, such as lack of opportunities for engagement, power imbalances, tokenism, lack of accessibility of engagement opportunities and experiences of racism and discrimination were also identified. CONCLUSION: This study increases the understanding of patient, family member, and caregiver engagement in policy related to PHC team integration and the barriers that currently exist in this engagement process. This information can be used to guide decision-makers on how to improve the delivery of integrated health services through PHC teams and enhance patient, family member, and caregiver engagement in PHC policy. PATIENT OR PUBLIC CONTRIBUTION: We would like to acknowledge the contributions of our patient partners, Brenda Jagroop and Judy Birdsell, who assisted with developing and pilot testing the interview guide. Judy Birdsell also assisted with the preparation of this manuscript. This study also engaged patients, family members, and caregivers to share their experiences with engagement in PHC policy.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".