Applying a Health Equity Lens to Virtual Primary Care for Socially Vulnerable Groups During the Covid-19 Pandemic
Bibliographic record
Abstract
In primary care, patients seek care for all their comorbidities, not just for a solitary, defining, major health condition. Making primary care available to all population segments helps maintain a community's or entire population's health. However, many disaster planning documents do not adequately consider social determinants of health and community diversity that underpin primary care and therefore fall short of planning for primary care delivery during a disaster. Vulnerability to disaster is more than simply physical presence during a natural hazard, but also considers social factors surrounding individuals and communities that dictate how likely they are to be harmed and suffer losses. To understand the utility of virtual primary care in a disaster setting for all groups, including socially vulnerable patients, this study was conducted in Calgary, Alberta, with data obtained from the Mosaic Primary Care Network (MPCN), one of 40 Primary Care Networks in Alberta. The data were collected within MPCN as part of routine research and process evaluation. Two datasets were obtained; one with patient-level data and one with provider-level data. A total of ~531,000 patient encounters were recorded between June 1, 2018, and June 30, 2022, to which a material deprivation index could be applied. Primary care delivery through virtual care favoured higher SES patients that could access technology, possessed digital literacy, and had low-barrier communication abilities. Select social groups were more affected than others; three of the most affected were older, male, and the most materially deprived. Systemic and individual-level barriers were identified. The findings of this study confirm that the individuals that comprise vulnerable groups do indeed engage with primary care differently than non-vulnerable groups and that a pivot to entirely virtual care in disaster situations does not take health equity into account.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".