Family physicians’ responses to personal protective equipment shortages in four regions in Canada: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Despite well-documented increased demands and shortages of personal protective equipment (PPE) during previous disease outbreaks, health systems in Canada were poorly prepared to meet the need for PPE during the COVID-19 pandemic. In the primary care sector, PPE shortages impacted the delivery of health services and contributed to increased workload, fear, and anxiety among primary care providers. This study examines family physicians' (FPs) response to PPE shortages during the first year of the COVID-19 pandemic to inform future pandemic planning. METHODS: As part of a multiple case study, we conducted semi-structured qualitative interviews with FPs across four regions in Canada. During the interviews, FPs were asked to describe the pandemic-related roles they performed over different stages of the pandemic, facilitators and barriers they experienced in performing these roles, and potential roles they could have filled. Interviews were transcribed and a thematic analysis approach was employed to identify recurring themes. For the current study, we examined themes related to PPE. RESULTS: A total of 68 FPs were interviewed across the four regions. Four overarching themes were identified: 1) factors associated with good PPE access, 2) managing PPE shortages, 3) impact of PPE shortages on practice and providers, and 4) symbolism of PPE in primary care. There was a wide discrepancy in access to PPE both within and across regions, and integration with hospital or regional health authorities often resulted in better access than community-based practices. When PPE was limited, FPs described rationing and reusing these resources in an effort to conserve, which often resulted in anxiety and personal safety concerns. Many FPs expressed that PPE shortages had come to symbolize neglect and a lack of concern for the primary care sector in the pandemic response. CONCLUSIONS: During the COVID-19 pandemic response, hospital-centric plans and a lack of prioritization for primary care led to shortages of PPE for family physicians. This study highlights the need to consider primary care in PPE conservation and allocation strategies and to examine the influence of the underlying organization of primary care on PPE distribution during the pandemic.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".