MétaCan
Menu
Back to cohort
Record W7093845384

9781773852096.pdf

2022· other· en· W7093845384 on OpenAlexaboutno aff

Bibliographic record

VenueOAPEN (The OAPEN Foundation) · 2022
Typeother
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsFidelityScale (ratio)Reliability (semiconductor)Consistency (knowledge bases)Sample (material)Schizophrenia (object-oriented programming)Set (abstract data type)Quality (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

The First Episode Psychosis Services Fidelity Scale (FEPS-FS 1.0) is a highly reliable scale that assesses the degree to which mental health teams deliver specialized evidence-based care to people experiencing a first episode psychosis. The scale comprises 35 components each rated on a 1 to 5 scale. It has been used in the United States, Canada and Europe. It can be used for on site fidelity reviews, remote fidelity assessment or self-report. Published papers document its psychometric features and allow comparisons with a representative sample of US programs. It is suitable for research, quality improvement and accreditation. The Manual provides a practical guide for scoring a FEPS program against the criteria set out in the fidelity scale. It is designed to increase the reliability and consistency of ratings across different sites and assessors. It includes a definition and rationale for each component, data sources, decision rules and a structured interview guide. There are also modules to support training the key informant and data abstractor. Templates support structured feedback to programs for quality improvement. The scale can be adjusted to rate care for different diagnostic groups including the schizophrenia spectrum disorders, bipolar disorder and those with an attenuated psychosis syndrome.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0070.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.8020.914

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.

Opus teacher head0.012
GPT teacher head0.229
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueOAPEN (The OAPEN Foundation)Same topicHistory of Computing TechnologiesFrench-language works237,207