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Record W7084150023 · doi:10.6084/m9.figshare.30038716

Supplementary Material 3. Articles Included in Review.pdf

2025· dataset· en· W7084150023 on OpenAlexaff

Bibliographic record

VenueFigshare · 2025
Typedataset
Languageen
FieldEnvironmental Science
TopicAgriculture, Water, and Health
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPsychological interventionTransfer of trainingSupervisorTransfer of learningOrganizational cultureIntervention (counseling)Work (physics)Knowledge transfer

Abstract

fetched live from OpenAlex

Under the happy-productive worker hypothesis, organizations invest significant resources in employee well-being with the expectation of organizational benefits. However, more evidence is needed to understand the extent wellness interventions generate mutual gains for both well-being and work outcomes. This review combines systematic and realist approaches to examine 154 individual-level wellness intervention studies and the contextual factors that enable—or limit—their success. Drawing from management training literature, we apply Holton’s model of learning transfer, which emphasizes the role of individual and contextual factors in shaping transfer motivation, transfer design, and the transfer climate. We find that wellness interventions consistently enhance employee well-being but do not reliably lead to improvements in workplace outcomes, such as performance. Our analysis identifies the program mechanisms that support training transfer and contribute to mutual gains: transfer design is enabled when interventions have program characteristics that reflect the work context; transfer motivation was bolstered when organizations gave thoughtful consideration of participants’ needs and engagement strategies; and transfer climate was enabled by factors like supervisor support and organizational culture that reinforced cultural fit. Theoretical and practical implications are discussed, emphasizing context-sensitive interventions that optimize wellness programs for learning transfer to enable mutual gains.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.917
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.9230.006

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 head0.019
GPT teacher head0.276
Teacher spread0.256 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2025
Admission routes1
Has abstractyes

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