The First Episode Psychosis Services Fidelity Scale as a Measure of Quality of Care
Bibliographic record
Abstract
Background: Fidelity scales are designed to assess the degree to which a program delivers the core components of evidence-based programs. The degree to which scales can predict outcomes-its predictive validity-is an important psychometric property. Fidelity scales have multiple applications, including in quality assurance and improvement. The purpose of this study was to assess the degree to which the First Episode Psychosis Services Fidelity Scale (FEPS-FS) assesses the 6 domains of quality of care (QoC) described in the Institute of Medicine (IOM)'s QoC framework. Study Design: A quality thematic analysis using a theoretical (deductive) approach was used to categorize the 36 items of the FEPS-FS according to the framework for evaluating QoC outlined by the IOM. Two coders independently reviewed and coded each item in duplicate. Frequency counts were used to summarize the characteristics of the FEPS-FS items across the IOM's quality domains. Study Results: Most FEPS-FS items reflect effectiveness (47.2%), efficiency (19.59%), and patient-centeredness (18.56%). Safety (7.22%), timeliness (5.15%), and equity (2.06%) were relatively underrepresented. Conclusions: The FEPS-FS can be considered as a measure of QoC that reflects all the IOM domains. As expected for a fidelity scale, the largest number of items assess effectiveness, while safety, timeliness, and equity were represented by fewer items. We identified potential items from the literature that could be used to increase the proportion of items in underrepresented quality domains in future iterations of the FEPS-FS or other fidelity scales.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".