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Record W2151912824 · doi:10.1002/hrdq.1059

Translating training science into practice: A study of managers' reactions to posttraining transfer interventions

2003· article· en· W2151912824 on OpenAlexaff
Philip Huint, Alan M. Saks

Bibliographic record

VenueHuman Resource Development Quarterly · 2003
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsSupervisorPsychological interventionIntervention (counseling)PsychologyTransfer of trainingProductivityInformation transferMedical educationApplied psychologyMedicineManagementPsychiatry

Abstract

fetched live from OpenAlex

Abstract The purpose of this study was to investigate managers' reactions to two posttraining transfer interventions (relapse prevention and supervisor support training) and two types of information about their effectiveness (utility analysis and research information). One hundred seventy‐four managers and students received one of four scenarios and were then asked if they would adopt the intervention as part of a training program to increase the productivity of clerical‐administrative staff. The results indicated no significant differences between the posttraining transfer interventions or information conditions, although there was a trend in favor of the supervisor support intervention; also, the utility analysis information was not as well received as the research information. In general, managers did not indicate a high acceptance of either the relapse prevention or the supervisor support intervention. The results are discussed in terms of the willingness of managers to adopt training research innovations and the implications for HRD research‐to‐practice.

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)0.014
metaresearch head score (Gemma)0.113
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 score0.014
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.113
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.118
GPT teacher head0.410
Teacher spread0.293 · 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

Citations30
Published2003
Admission routes1
Has abstractyes

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