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Record W4285685852 · doi:10.2196/35290

Study Protocol and Preliminary Results of the Impact of Occupational Health Workers' Activities on Their Health: Nationwide Prospective Internet-Based Survey

2022· article· en· W4285685852 on OpenAlexvenueno aff
Kazunori Ikegami, Yasuro Yoshimoto, Hiroka Baba, Shingo Sekoguchi, Hajime Ando, Akira Ogami

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMental healthData collectionBaseline (sea)Occupational safety and healthGerontologyEnvironmental healthFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Owing to the impact of the COVID-19 pandemic, work environments and systems, as well as occupational health measures or activities that fall within our research field, are constantly changing. It is necessary to assess the impact of these changes on the physical and mental health of workers. OBJECTIVE: To assess how occupational health measures affect the health of workers, we conducted a baseline, longitudinal internet-based survey among Japanese workers in October 2021 and additionally scheduled 2 follow-up surveys for 2022 and 2023. We describe the details of the protocol of the work systems and health internet research (WSHIR) study, provide an overview of the results of the baseline survey, and discuss the study procedures and data used in the study. METHODS: This prospective cohort study was conducted online among internet monitors. The baseline survey was conducted from October 1 to 7, 2021. This study targeted those who were working and between the ages of 20 and 69 years. A total of 5111 respondents who passed the screening survey and proceeded to the main survey were enrolled according to collection units organized by sex and age. For the screening and main surveys, the questionnaire consisted of 9 and 33 items with 9 and 55 questions, respectively. Consistency and completeness checks were performed after the questionnaires were submitted. We compared basic characteristics, such as sex, age group, educational background, and marital status, among all participants, including those who withdrew from the analysis. RESULTS: Of the 5111 initial survey respondents, 571 (11.2%) were considered fraudulent. The data of the remaining 4540 (88.8%) participants (2273, 50.1%, males; 2267, 49.9%, females) included in the analysis were well balanced across participant sex and age groups according to the sampling plan because there was no significant difference by sex and age group using the chi-square test for checking the distribution bias of the participants (P=.84). Compared to female participants, male participants tended to be more likely to be managers and supervisors (323, 14.2%, males; 86, 3.8%, females), to work in a secondary industry (742, 32.6%, males; 357, 15.7%, females), and to have an annual income of ≥5 million yen (976, 42.9%, males; 429, 18.9%, females). For the evaluation of a psychological indicator, Kessler 6 (K6) score, by sex and age group, the characteristics of the score distribution of the included participants were similar to those reported in previous studies. CONCLUSIONS: This study presents a protocol and overview of the results of an internet-based occupational health survey of workers. Using the results of this survey, we hope to evaluate the changes in occupational health activities and their impact on workers' health while controlling for the COVID-19 pandemic.

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)0.024
metaresearch head score (Gemma)0.016
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: Observational
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.024
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.016
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0170.007

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 head0.158
GPT teacher head0.556
Teacher spread0.398 · 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
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

Citations3
Published2022
Admission routes1
Has abstractyes

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