The impact of COVID-19 pandemic public health measures on the practice of primary care allied health professionals in Manitoba and Ontario
Bibliographic record
Abstract
The purpose of this study was to investigate how the public health measures implemented in Manitoba and Ontario during waves 1 and 2 of the COVID-19 pandemic impacted allied health professionals working in primary care settings. This study used a case study methodology to develop four cases, two allied health professionals from Manitoba and two allied health professionals of the same professions from Ontario. Two methods of data collection were used, diary entry and interview. Diary entry data was collected between March 2020 and August 2020. Interviews were conducted in December 2020. This study’s approach to data analysis was to use the framework analysis to apply a conceptual framework, specifically the Roy Adaptation Model. The Roy Adaptation Model encompasses four adaptive modes: role function, interdependence, group identity, and physiological. The results section presents how each of these modes were operationalized for each case. The public health measures affected the role function mode more significantly than the other modes. All participants experienced role disruptions with redeployment and role change with the transition to remote and virtual care. The allied health providers in both provinces experienced role reductions with limitations in their ability to practice their primary role. The implemented COVID-19 public health measures led providers to work within their roles in an adapted capacity during the length of the pandemic. The greatest differences between the experiences of providers in Ontario versus Manitoba was the timeline of events and the response of the provincial governments. This study highlights how macro policies influence the day-to-day of healthcare workers.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".