Primary prevention and screening during the COVID-19 pandemic: Qualitative findings from the BETTER WISE project
Bibliographic record
Abstract
Context: The COVID-19 pandemic challenged healthcare systems worldwide and disrupted primary care through redeployment of healthcare resources to COVID-19 priorities resulting in inaccessible prevention and screening services. The BETTER WISE project involved a comprehensive, evidence-based intervention for patients 40 to 65 years of age to proactively address cancer and chronic disease prevention and screening (CCDPS), including associated lifestyle risks, cancer surveillance and screening for financial difficulty. Patients were invited for a 1-hour prevention visit with a Prevention Practitioner (PP), a member of the primary care team with specialized training in CCDPS and cancer surveillance, to discuss their CCDPS and cancer surveillance status, set S.M.A.R.T goals for their health, and make links to community resources as appropriate. Objective: To describe how the COVID-19 pandemic impacted implementation of the BETTER WISE intervention. Methods: Qualitative study - Seventeen focus groups and 48 key informant interviews were conducted with 132 primary care team members (PPs, physicians, allied health professionals, and clinic staff) at three time points in the study. Qualitative data also included 585 written feedback forms from patients, field notes and memos. A thematic analysis using a constant comparative method focused on the impact of the pandemic on BETTER WISE was employed. Setting: Thirteen primary care settings (urban, rural, and remote) across 3 Canadian Provinces (Alberta, Ontario, and Newfoundland and Labrador). Results: Four themes emerged from the data regarding how the COVID-19 pandemic impacted the BETTER WISE study: 1) Switch of in-person visits to visits over the phone; 2) Lack of access to preventive care and delays of screening tests; 3) Changes in primary care providers’ availability and priorities; 4) Mental health impacts of the pandemic on patients and primary care providers. Conclusions: The COVID-19 pandemic significantly impacted primary care as aggressive shifts in priorities were required and non-essential prevention and screening services were made unavailable. Despite structural, procedural, and personal challenges throughout different waves of the pandemic, the primary care clinics participating in BETTER WISE were able to complete the study. Our results underscore the importance of the role of primary care providers in adapting to changing circumstances and support of patients in challenging times.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 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".