Identification of Implementation Strategies Used for the Circle of Security-Virginia Family Model Intervention: Concept Mapping Study
Bibliographic record
Abstract
BACKGROUND: A reoccurring finding from health and clinical services is the failure to implement theory and research into practice and policy in appropriate and efficient ways, which is why it is essential to develop and identify implementation strategies, as they constitute the how-to component of translating and changing health practices. OBJECTIVE: The aim of this study was to provide a systematic and comprehensive review of the implementation strategies that have been applied for the Circle of Security-Virginia Family (COS-VF) model by developing an implementation protocol. METHODS: First, informal interviews and documents were analyzed using concept mapping to identify implementation strategies. All documentation from the Network for Infant Mental Health's work with COS-VF was made available and included for analysis, and the participants were interviewed to validate the findings and add information not present in the archives. To avoid lack of clarity, an existing taxonomy of implementation strategies, the Expert Recommendations for Implementing Change, was used to conceptualize (ie, name and define) strategies. Second, the identified strategies were specified according to Proctor and colleagues' recommendations for reporting in terms of seven dimensions: actor, the action, action targets, temporality, dose, implementation outcomes, and theoretical justification. This ensures a full description of the implementation strategies and how these should be used in practice. RESULTS: Ten implementation strategies were identified: (1) develop educational materials, (2) conduct ongoing training, (3) audit and feedback, (4) make training dynamic, (5) distribute educational materials, (6) mandate change, (7) obtain formal commitments, (8) centralize technical assistance, (9) create or change credentialing and licensure standards, and (10) organize clinician implementation team meetings. CONCLUSIONS: This protocol provides a systematic and comprehensive overview of the implementation of the COS-VF in health services. It constitutes a blueprint for the implementation of COS-VF that supports the interpretation of subsequent evaluation studies, facilitates knowledge transfer and reproducibility of research results in practice, and eases the replication and comparison of implementation strategies in COS-VF and other interventions.
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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 |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | 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 | 0.087 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".