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Record W4308916752 · doi:10.1177/10982140211008978

A Comparison of Fidelity Implementation Frameworks Used in the Field of Early Intervention

2022· article· en· W4308916752 on OpenAlex
Colombe Lemire, Michel Rousseau, Carmen Dionne

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Evaluation · 2022
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsFidelityConceptualizationConceptual frameworkIntervention (counseling)Field (mathematics)Management scienceComputer scienceQuality (philosophy)Knowledge managementPsychologySociologyEngineeringSocial science

Abstract

fetched live from OpenAlex

Implementation fidelity is the degree of compliance with which the core elements of program or intervention practices are used as intended. The scientific literature reveals gaps in defining and assessing implementation fidelity in early intervention: lack of common definitions and conceptual framework as well as their lack of application. Through a critical review of the scientific literature, this article aims to identify information that can be used to develop a common language and guidelines for assessing implementation fidelity. An analysis of 46 theoretical and empirical papers about early intervention implementation, published between 1998 and 2018, identified four conceptual frameworks, in addition to that of Dane and Schneider. Following analysis of the conceptual frameworks, a four-component conceptualization of implementation fidelity (adherence, dosage, quality and participant responsiveness) is proposed.

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.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.110
GPT teacher head0.561
Teacher spread0.451 · 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