Perspectives of Clinicians and Program Decision-Makers on the Role of Fidelity Monitoring in Coordinated Specialty Care: A Qualitative Study
Bibliographic record
Abstract
Introduction: The purpose of this study is to describe how Coordinated Specialty Care program clinicians and decision-makers experience fidelity monitoring and what components of the fidelity model are most relevant to them. Methods: Using an instrumental case study design informed by the Exploration Preparation Implementation Sustainment framework, data were collected using semi-structured interviews. A constant comparison approach with both inductive and deductive coding was used. Results: Fourteen participants representing 3 regions of the United States were interviewed. The analysis resulted in 22 codes and 5 themes, (1) the fidelity review process, (2) facilitators and barriers to fidelity monitoring, (3) the impact of monitoring fidelity, (4) pros and cons of fidelity monitoring, and (5) shared decision making and flexibility. Across participants, flexible implementation of the model components and shared decision-making were considered central to implementation. Fidelity monitoring conflicted with these goals in some experiences. Clinicians and program decision-makers varied in the experience of facilitators and barriers to fidelity, yet lack of training and education was an important barrier. Conclusion: Although participants shared prioritization of flexibility and shared decision-making as core needs, the role of fidelity in achieving the goals of coordinated specialty care was unclear and potentially conflicting. Better alignment of fidelity monitoring with coordinated specialty care central tenants could improve the experience of both implementers and clients.
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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 | 0.048 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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, 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".