Operationalising Patient Engagement Through the Alberta Cancer Diagnosis Initiative: Recruitment Strategies for Diverse Populations in Health System Improvement
Bibliographic record
Abstract
BACKGROUND: Engaging diverse populations is critical for designing effective healthcare initiatives. However, strategies for recruiting participants to ensure meaningful engagement, particularly among harder-to-reach groups, remain underexplored. This study examines recruitment approaches used in the Alberta Cancer Diagnosis Initiative (ACDI) in Alberta, Canada, to address challenges in cancer diagnosis. METHODS: A qualitative study, including seven interviews and four focus groups with 10 members of the ACDI project team (none of them patients or community members) and a review of 20 internal ACDI documents, was undertaken. Data were analysed using inductive coding, focusing on identifying recruitment strategies for engaging participants from diverse groups and considerations for facilitating recruitment. RESULTS: Early commitment to diversity and relationship-building informed recruitment strategies, including working with community brokers and health system navigators. Barriers included a limited time within the grant cycle to develop strong relationships with population groups. The team's capacity to learn from emerging issues, like intersectionality and language, was crucial to developing an adaptive approach to recruitment. CONCLUSIONS: Tailored and adaptive strategies, particularly broker- and community-based approaches, are crucial for engaging diverse groups. Lessons learned can inform future initiatives seeking to involve these groups in healthcare decision-making and programme development. PATIENT OR PUBLIC CONTRIBUTION: Patients and community members were actively involved in the ACDI during its planning and development. Their contributions informed engagement activities, ensuring the inclusion of diverse perspectives. This study examined the ACDI project team's perspectives on recruitment strategies and lessons learned, highlighting the importance of adaptive, community-based approaches in engaging diverse participants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".