Implementing one-at-a-time therapy services as one core component of a provincial stepped care model within Prince Edward Island's community mental health and addictions services: an implementation process synthesis
Bibliographic record
Abstract
Providers within Health PEI's Community Mental Health and Addictions Services completed online asynchronous courses in one-at-a-time (OAAT) therapy and Stepped Care 2.0 (SC2.0) as part of an initiative to implement a provincial stepped care model. This thesis: 1) mapped the OAAT therapy implementation process using three frameworks: the Active Implementation Frameworks, Expert Recommendations for Implementing Change, and the Consolidated Framework for Implementation Research; 2) measured provider attitude and knowledge; 3) explored providers� implementation experiences; and 4) quantified OAAT therapy delivery. The study used a mixed-methods, single-cohort, observational design with two interventions. Surveys were distributed to providers at five time points over four months, and researchers interviewed seven program champions. Implementation data was abstracted from Health PEI, stakeholder, and research team documentation. Providers (N = 72) demonstrated an increase in SC2.0 knowledge and endorsed agreement for the acceptance, appropriateness, and feasibility of SC2.0/OAAT therapy, including organizational readiness (e.g., compatibility, knowledge and skills, leadership, and program champions). Interview themes aligned with existing implementation strategies (e.g., co-design, communication, and mentorship), which were considered factors for implementation sustainability. Providers delivered 3,746 OAAT therapy sessions from 2023 to 2024, showcasing the cumulative efforts made by Health PEI, providers, and stakeholders.
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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 | 0.031 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".