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Record W4392659278 · doi:10.1080/01608061.2024.2326020

An Evaluation of a Staff Management Strategy to Minimize Reactivity in Procedural Fidelity of Intervention Implementers

2024· article· en· W4392659278 on OpenAlexaff
Claudia C. Reyes, Raymond G. Miltenberger, Rasha R. Baruni

Bibliographic record

VenueJournal of Organizational Behavior Management · 2024
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFidelityIntervention (counseling)Organizational behavior managementPsychologyProcess managementBusinessMedicineNursingComputer scienceOrganizational commitmentOrganizational behavior and human resourcesSocial psychology

Abstract

fetched live from OpenAlex

The purpose of the current study was to evaluate reactivity to observation and increase procedural fidelity in observer absent conditions by delivering feedback to participants following observer absent observation sessions. Two of the three participants increased their rate of positive social engagements above criterion level following behavioral skills training (BST) and feedback in the observer present condition, but this increase was not seen in the observer absent condition. After the delivery of feedback in the observer absent condition, the participants exhibited an increase procedural fidelity. The third participant responded above criterion in the observer present condition during baseline and so went straight into feedback in the observer absent condition following BST and showed an increase in performance also. Responding during generalization probes suggested that feedback should be delivered in all contexts in which procedural fidelity is expected of implementers.

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)0.012
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.156
GPT teacher head0.426
Teacher spread0.270 · 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 designObservational
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

Citations5
Published2024
Admission routes1
Has abstractyes

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