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Record W2765651113 · doi:10.1177/0741932517736515

Factor Validation of a Fidelity of Implementation Measure for Social Behavior Systems

2017· article· en· W2765651113 on OpenAlexaff
Michelle M. Massar, Kent McIntosh, Sterett H. Mercer

Bibliographic record

VenueRemedial and Special Education · 2017
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFidelityMeasure (data warehouse)Confirmatory factor analysisPsychological interventionPsychologyComputer scienceFactor (programming language)Structural equation modelingMachine learningData mining

Abstract

fetched live from OpenAlex

Assessing fidelity of implementation of school-based interventions is a critical factor in successful implementation and sustainability. The Tiered Fidelity Inventory (TFI) was developed as a comprehensive measure of all three tiers of School-Wide Positive Behavioral Interventions and Supports (SWPBIS) and is intended to measure the extent to which the core features of SWPBIS are implemented with fidelity. The purpose of this study was to assess the extent to which the TFI can be used as one measure of all three tiers, three separate measures of individual tiers, or as a more granular level of fidelity that measures implementation on 10 subscales across the tiers. A confirmatory factor analysis was conducted to validate the factor structure of the TFI. Results indicate that the TFI is a valid measure of fidelity of implementation of SWPBIS and can be used to measure implementation by subscales, tiers, and as a comprehensive assessment of all three tiers.

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 imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.082
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.082
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.333
GPT teacher head0.467
Teacher spread0.133 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations20
Published2017
Admission routes1
Has abstractyes

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