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Record W2157422824 · doi:10.1111/1464-0597.00032

The Effect of Mental Practice and Goal Setting as a Transfer of Training Intervention on Supervisors’ Self‐efficacy and Communication Skills: An Exploratory Study

2000· article· en· W2157422824 on OpenAlexaff
Lucie Morin, Gary P. Latham

Bibliographic record

VenueApplied Psychology · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologySupervisorApplied psychologyGoal settingIntervention (counseling)Self-efficacyPsychological interventionClinical psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Mental practice, where goal setting was either implicit or explicit, was investigated in a pulp and paper mill as a post‐training intervention with regard to self‐efficacy and the transfer of newly taught communication skills to the work setting. Six months after the supervisors had been trained, a 2×2 ANCOVA showed that self‐efficacy was significantly higher for the supervisors who engaged in either mental practice or in mental practice combined with goal setting than for those in the goal setting only or control conditions. Self‐efficacy correlated significantly with goal commitment and communication skills on the job. Hierarchical regression analysis indicated that the supervisor’s imagery skills moderated the effect of mental practice on self‐efficacy. Both the supervisors in the mental practice and in the goal setting and mental practice conditions were observed by peers to have improved their communication behaviour on the job. No change in communication behaviour was observed on the part of supervisors who set goals but did not engage in mental practice or were assigned to the control group.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.307
Teacher spread0.294 · 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

Citations103
Published2000
Admission routes1
Has abstractyes

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