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Record W2883885332 · doi:10.1097/jom.0000000000001402

Process Evaluation of a Digital Platform-Based Implementation Strategy Aimed at Work Stress Prevention in a Health Care Organization

2018· article· en· W2883885332 on OpenAlexfundno aff
Bo M. Havermans, Cécile R. L. Boot, Evelien Brouwers, I.L.D. Houtman, Johannes R. Anema, Allard J. van der Beek

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2018
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
FundersInstituut GakInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du TravailVrije Universiteit AmsterdamZonMwPfizer
KeywordsContext (archaeology)RestructuringProcess managementSoftware deploymentWork (physics)Process (computing)Economic shortageBusinessMental healthManagement strategyOperations managementMedicineNursingKnowledge managementComputer scienceEngineeringGovernment (linguistics)

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective was to evaluate the process and feasibility of a digital platform-based implementation strategy aimed at work stress prevention. METHODS: The process evaluation was performed alongside a controlled trial within a health care organization, in the experimental group (N = 221). Mental models, context, and barriers and facilitators were measured. In addition, dose delivered, reach, and dose received were assessed. RESULTS: Employees reported relatively high readiness for change. Personnel shortage and a recent restructuring of the organization hindered use of the strategy. Low management support and high turnover stagnated strategy deployment. Dose delivered was 13/15, reach was 11/15, and dose received was 5/15. CONCLUSIONS: Strategy implementation was moderately successful, as sustained strategy use by the teams appeared to be a challenge. The strategy can be feasible with sufficient management support and resources.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.058
GPT teacher head0.447
Teacher spread0.390 · 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 teacher head, 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

Citations40
Published2018
Admission routes1
Has abstractyes

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