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Record W2130225889 · doi:10.1080/13678868.2011.542897

Workplace learning strategies, barriers, facilitators and outcomes: a qualitative study among human resource management practitioners

2011· article· en· W2130225889 on OpenAlexaffabout
Paula Crouse, Wendy Doyle, Jeffrey D. Young

Bibliographic record

VenueHuman Resource Development International · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsWorkplace learningHuman resourcesHuman resource managementKnowledge managementFace (sociological concept)Training and developmentQualitative researchGovernment (linguistics)Health professionalsHealth carePsychologyProfessional developmentBusinessMedical educationPublic relationsSociologyManagementMedicinePedagogyPolitical scienceWork (physics)Engineering

Abstract

fetched live from OpenAlex

Recently the role of human resource management (HRM) practitioners has become more professionalized and more strategic. Consequently, HRM practitioners have had to develop new competencies in areas such as change management, influence and technology. Workplace learning, which is important for professional development, is examined for 13 HRM practitioners in government, healthcare, post-secondary education and business organizations in the Halifax Regional Municipality area. Of particular interest were learning strategies, barriers to and facilitators of learning and outcomes of learning. To obtain rich data, practitioners were interviewed face to face using an interview guide. Results indicated that these practitioners are mostly similar to other professional groups in terms of workplace learning, with a few key differences. The similarities and differences are presented, and implications of these findings for HRM practitioners and future directions for research are discussed.

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)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0090.005
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.002
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 head0.059
GPT teacher head0.386
Teacher spread0.328 · 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 designQualitative
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

Citations129
Published2011
Admission routes2
Has abstractyes

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