Evidence map›Paper›PMID 38803623›Full record

ArticleRisk management and healthcare policy2024

Association Between Suboptimal Health Status and Health-Related Productivity Loss in Primary Healthcare Workers in China: a Cross-Sectional Survey.

Liang Lu, Yongfa Chen

Abstract read
In one paragraph

Article in Risk management and healthcare policy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Liang LuSchool of International Pharmaceutical Business, China Pharmaceutical University, Nanjing City, Jiangsu, People's Republic of China.ORCID 0000-0001-8900-3555
Yongfa ChenSchool of International Pharmaceutical Business, China Pharmaceutical University, Nanjing City, Jiangsu, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to clarify the association between suboptimal health status and health-related productivity loss among primary healthcare workers in China. Material and Methods: A field questionnaire survey was conducted with a multistage sampling among primary healthcare workers. The data on sub-health and health-related productivity loss were collected using the Sub-health Measurement Scale Version 1.0 and Work Productivity and Activity Impairment Questionnaire: General Health, respectively. Ordinary least squares regression was used to evaluate the association of the suboptimal health and health-related productivity loss. Subgroup analyses were performed by occupation (physician and nurse). Results: Front-line primary healthcare workers (N = 1709) from 31 provinces in China responded to the survey. Of all participants, 73.43% experienced suboptimal health. The status of being in physical suboptimal health (Coef. = 0.050, p < 0.001, 95% CI = [0.031,0.070]) and mental suboptimal health (Coef. = 0.040, p < 0.001, 95% CI = [0.020,0.059]) status exerted significant effect on the extent of health-related productivity loss among primary healthcare workers in China. Nurses in social adaptation suboptimal health status (Coef. = 0.030, p = 0.027, 95% CI = [0.003,0.057]) had significant effect on health-related productivity loss. Conclusion: The prevalence of suboptimal health is high among the respondents. Providing support for primary healthcare workers from bio-psycho-social aspects is an effective measure to promote their occupational health and improve their productivity.

Indexed as

cross-sectional surveyhealthcare workersoccupational healthpresenteeismproductivity

Identifiers

PMID38803623
PMCPMC11129702

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.