The First Episode Psychosis Services Fidelity Scale 1.0: Review and Update
Bibliographic record
Abstract
Abstract The First Episode Psychosis Fidelity Scale, first published in 2016, is based on a list of essential components identified by systematic reviews and an international consensus process. The purpose of this paper was to present the FEPS-FS 1.0 version of the scale, review the results of studies that have examined the scale and provide an up-to-date review of evidence for each component and its rating. The First Episode Psychosis Services Fidelity Scale 1.0 has 35 components, which rate access and quality of health care delivered by early psychosis teams. Twenty-five components rate service components, and 15 components rate team functioning. Each component is rated on a 1–5 scale, and a rating of 4 is satisfactory. The service components describe services received by patients rather than staff activity. The fidelity rater completes ratings based on administrative data, health record review, and interviews. Fidelity raters from two multicenter studies provided feedback on the clarity and precision of component definitions and ratings. When administered by trained raters, the scale demonstrated good to excellent interrater reliability. The selection of components can be adjusted to rate programs serving patients with bipolar disorder or an attenuated psychosis syndrome. The scale can be used to assess and improve the quality of individual programs, compare programs and program networks. Researchers can use the scale as an outcome measure for implementation studies and as a process measure for outcome studies. Future research should focus on demonstrating predictive validity.
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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".