Implementing patient navigator programmes within a hospital setting in Toronto, Canada: A qualitative interview study
Bibliographic record
Abstract
Objectives:This study sought to identify the organisation and system level barriers and facilitators influencing the implementation of patient navigator programmes in one acute care hospital system in Toronto, Canada.Methods:A qualitative descriptive approach informed by the Consolidated Framework for Implementation Research. Data were collected using in-depth interviews and analysed thematically.Results:Thirty-eight individuals participated in interviews (17 community, 21 acute care hospital), including 24 frontline clinicians and 14 programme directors, health care leaders and managers. Implementation of patient navigator programmes was dependent on: (1) a clear consensus on the unique need for patient navigators; (2) champions to promote patient navigation; (3) programme ownership and accountability; (4) external system and organisational landscape and (5) implementation climate. Appropriate mechanisms of communication were found to have impacted each factor as a barrier or facilitator to programme implementation.Conclusion:Strategies for implementing patient navigator programmes into hospital clinical practice should include incorporating evidence to support the programme, considering mechanisms to enable collaborative communication, and the integration of frameworks to facilitate programme integration into the current practices within the organisation.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".