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Record W3169384275 · doi:10.2196/29869

Integrated Prevention at Work: Protocol for a Concept Analysis

2021· article· en· W3169384275 on OpenAlexaffvenue
Alexandra Lecours, Marie-Ève Major, Claude Vincent, Valérie Lederer, Marie‐Ève Lamontagne

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversité du Québec en OutaouaisUniversité LavalUniversité de SherbrookeUniversité du Québec à MontréalUniversité du Québec à Trois-RivièresCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsOperationalizationKnowledge managementComputer scienceConceptualizationData collectionPsychologySociology

Abstract

fetched live from OpenAlex

BACKGROUND: Integrated prevention at work promises to eliminate the boundaries between primary, secondary, and tertiary prevention actions taken by stakeholders in the world of work. It is receiving increasing attention from the scientific community because of its concerted and harmonized approach, which promotes employment access, return, and healthy long-term continuation. Although promising, integrated prevention is not yet well-defined, which makes it difficult to operationalize. OBJECTIVE: This manuscript exposes the protocol of a study aiming to conceptualize integrated prevention at work on the basis of scientific and experiential knowledge. METHODS: Using a concept analysis research design, data collection has been planned in 2 parts. A meta-narrative literature review will first be conducted to document how integrated prevention has been defined in the literature. Then, phone interviews will be conducted with key informers (ie, managers, workers, ergonomists, occupational therapists, psychologists, physiotherapists, union and insurance representatives) to document their viewpoints and understanding of integrated prevention at work. Qualitative data gathered during these 2 parts of research will be analyzed using template analysis, which allows data from literature and empirical collection to be analyzed simultaneously. The analysis will bring out the points of convergence, divergence, and complementarity between the information gleaned from literature and key informers' experiences to arrive at a conceptualization of integrated prevention at work by identifying its uses, attributes, antecedents, and consequences. As a final step, validation and interpretation with a TRIAGE (Technique for Research of Information by Animation of a Group of Experts) group will be carried out in collaboration with the key informers to identify the tools for the implementation of integrated prevention at work and promote workers' health and safety. RESULTS: This study is expected to offer a contemporary conceptualization of integrated prevention at work that clearly lays out the variables of this concept and elicits the viewpoints of the different stakeholders. CONCLUSIONS: This study will contribute to the advancement of knowledge about the professional injury prevention continuum. The clear identification of the uses, attributes, antecedents, and consequences of integrated prevention at work will offer concrete tools to stakeholders to implement innovative and promising approaches to integrated prevention at work. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/29869.

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)0.098
metaresearch head score (Gemma)0.139
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.127
Threshold uncertainty score0.519

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0980.139
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0060.007
Science and technology studies0.0070.004
Scholarly communication0.0060.005
Open science0.0040.005
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.1270.032

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 head0.580
GPT teacher head0.724
Teacher spread0.144 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations1
Published2021
Admission routes2
Has abstractyes

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