Thinking and Enacting the Patient Medical Home Under Pandemic Conditions: A Qualitative Study From Primary Care in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: The COVID-19 (C19) pandemic shocked primary care systems around the world. Those systems responded by supporting patients in the community, and acute care facilities in crisis. In Canada, the Patient Medical Home (PMH) is a widely adopted care model that aims to operationalize the tenets and principles of Primary Health Care (PHC) as developed since the Alma-Ata Declaration. This paper describes how personnel working in and with Primary Care Networks (PCNs) in Alberta, Canada deployed the PMH model and its underlying PHC principles to frame and respond to the C19 shock. METHODS: Using purposive and snowball sampling techniques, we interviewed 57 participants who worked in public health and primary care, including community-based family physicians. We used interpretive description to analyze the interviews. RESULTS: PCN staff and physicians described how the PMH model was foundational to normal operations, and how C19 responses were framed by the patient-centric, team-delivered, and continuous care principles the model shares with PHC. Specifically, participants described ensuring access to care, addressing the social determinants of health, being patient centered, and redeploying and expanding PHC teams to accomplish these goals. DISCUSSION: Delivering PHC through the PMH allowed physicians and allied health staff to deliver patient-centered, team-based, holistic bio-medical services to Albertans. In tailoring services to meet the specific social and health needs of the populations served by each PCN, healthcare providers were able to ensure relevant support remained available and accessible.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.016 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".