Implementing Primary and Community Care (PACC) Mapping to support COVID-19 vaccine uptake in western Canada
Bibliographic record
Abstract
Context: The COVID-19 pandemic highlighted health inequities and disparities across Canada. Meaningful community engagement and tailored supports were required to reduce barriers and ensure more equitable access to COVID-19 immunization. Objective: To engage communities in co-creating local immunization uptake solutions and foster community relationships to tackle post-pandemic challenges. Study Design and Analysis: Facilitators were trained and workshops conducted using patient personas to develop community solutions to address immunization access barriers and improve vaccine uptake. Surveys, interviews and informal feedback methods were used to evaluate the project. Setting or Dataset: Trained facilitators and communities across western Canada. Population Studied: Communities with low immunization uptake. Intervention/Instrument: Primary and Community Care (PACC) Mapping method tailored to the COVID-19 immunization context (Immunization PACC; immPACC). Outcome Measures: Outcomes were evaluated using the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework. Results: The Reach and Effectiveness of training facilitators in the PACC method was successful. A network of 54 facilitators were trained, reporting confidence (n = 45, mean 7.3, scale 1-10) and intention (n = 35/45, 78%, likely/very likely) to use the method. Adoption and Implementation of immPACC Mapping was slowed by reallocation of facilitators to other emergency response roles, and changing contextual factors. Despite this, eight immPACC Mapping sessions were delivered (April 2021-June 2022) in three provinces (Manitoba, Alberta, British Columbia), involving 12 facilitators and 77 participants. Findings highlighted the success of bringing interdisciplinary community stakeholders together to collaborate on local solutions, identify and build relationships with new partners, and facilitate action to implement patient-centred ideas. Maintenance of the method is ongoing, with facilitators in British Columbia and Manitoba using the PACC method to support health service planning at the local level. Conclusions: The PACC Mapping method was successfully used to bring a community-level focus into immunization planning. Despite challenges, community stakeholders created and implemented local solutions to support immunization uptake, and the project helped build capacity for using the PACC method to support future challenges in healthcare.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".