Exploring Determinants of Effective Implementation of an Innovation Within Health Care: Qualitative Insights from Program Champions on Implementing One-at-a-Time Therapy Within Addictions and Mental Health Services in New Brunswick
Bibliographic record
Abstract
Government of New Brunswick implemented One-at-a-Time (OAAT) therapy, a single-session approach to care, within Addiction and Mental Health (A&MH) services. We conducted interviews to understand determinants of implementation from program champions. Champions of the OAAT therapy implementation (N = 19; Child/Youth n = 8, Adult n = 11) working within A&MH services and school districts were recruited through the provincial implementation team. Transcripts were synthesized using thematic analysis. Determinants were organized as facilitators and barriers in accordance with the Consolidated Framework for Implementation Research (CFIR). Thematic analysis resulted in 18 themes and 5 recommendations. Facilitators within the inner setting included: (1) need for change and perceived benefits of OAAT therapy; (2) compatibility of OAAT therapy with previous practice and service processes; and (3) support received from champions and colleagues. Insufficient resources (e.g., staff and physical infrastructure), and a culture that favored long-term therapy were barriers. Navigating age of consent, and implementation around COVID-19 were barriers within the outer setting. Facilitators within the implementation process domain included: (1) interconnected teams across sites, regions and the province; (2) collaborative implementation planning; (3) flexibility to tailor implementation at sites; and (4) mentorship provided by champions. Insufficient standardization of the implementation and limited representation among affected parties (e.g., community partners) were barriers within the implementation process. This study elucidated determinants that influenced implementation of a new service delivery within an Eastern Canadian provincial health care system. Findings can serve as a heuristic for organizations looking to enact similar implementation initiatives.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".