Describing implementation outcomes for a virtual community of practice: The ECHO Ontario Mental Health experience
Bibliographic record
Abstract
BACKGROUND: Project ECHO is a virtual education model aimed at building capacity among healthcare providers to support optimal management for a range of health conditions. The expansion of the ECHO model, further amplified by the pandemic, has demonstrated an increased need to evaluate implementation success to ensure that interventions are implemented as planned. This study describes how Proctor et al.'s implementation outcomes (acceptability, adoption, appropriateness, costs, feasibility, fidelity, penetration, and sustainability) were adapted and used to assess the implementation of ECHO Ontario Mental Health (ECHO-ONMH), a mental health-focused capacity-building programme. METHODS: Using Proctor et al.'s implementation outcomes, the authors developed an implementation outcomes framework for ECHO-ONMH more generally. Using this, outcome measures and success thresholds were identified for each outcome for the ECHO-ONMH context, and then applied to evaluate the implementation of ECHO-ONMH using data from the first 4 years of the programme. RESULTS: An ECHO-ONMH implementation outcomes framework was developed using Proctor's implementation outcomes. ECHO-ONMH adapted implementation outcomes suggest that ECHO-ONMH was implemented successfully in all domains except for penetration, which only had participation from 13/14 regions. Acceptability, appropriateness and adoption success thresholds were surpassed for all 4 years, showing strong signs of sustainability. The programme was deemed feasible all 4 years and was found to be more cost-effective. ECHO-ONMH also showed high rates of fidelity to the ECHO model, and high rates of penetration. CONCLUSIONS: This is the first study to use Proctor et al.'s implementation outcomes to describe implementation success for a virtual capacity-building model. The proposed ECHO implementation outcomes framework provides a base for similar interventions to evaluate implementation success, which is an important precursor to understanding learning, service or health outcomes related to the model. Additionally, these findings can act as a benchmark for other international ECHOs and educational programmes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.027 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".