The Importance of an Effective Implementation Team Learning From Failure
Bibliographic record
Abstract
• Emphasizing the all encompassing, system wide involvement required for EBP implementation, scalability, and sustainability • Highlighting the impact of a team (or lack of a team) managing the process of implementation and rollout of the EBP • Defining the role of an implementation team Objectives of this talk © Treatment Implementation Collaborative, LLC 2011 :: Page 2 • Observation across 32 large scale implementation projects (multiple facilities, 4 or more treatment teams, clinical teams/ leadership involvement with access to executive leadership) • Spans continuum of care, length of stay, and target populations • Child and adolescent through standard adult services • International locations including state and county systems, for profit /not for profit systems in the US, provincial government and large hospital systems in Canada, and regional and national initiatives with District Health Boards in New Zealand Orientation to Context of Observation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.108 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".