Evidence map›Paper›PMID 42494856›Full record

ArticleFrontiers in rehabilitation sciences2026

Stakeholder perspectives on job analysis for disability-inclusive sewing employment in China: a qualitative content analysis.

Huiling Hu, Ziyu Wan, Peter H F Ng, Karen P Y Liu, Eugene Yujun Fu, Andy S K Cheng

Abstract read
In one paragraph

Article in Frontiers in rehabilitation sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Huiling HuDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.
Ziyu WanDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.
Peter H F NgDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.
Karen P Y LiuDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.
Eugene Yujun FuCentre for Learning, Teaching and Technology (LTTC), The Education University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Andy S K ChengDepartment of Health and Physical Education, The Education University of Hong Kong, Hong Kong, Hong Kong SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study explored stakeholder-informed perspectives on sewing employment in China, with a focus on employment-related factors, critical work tasks, and job demand domains. The findings were intended to inform future vocational evaluation and the development of AI-enhanced job analysis approaches for people with disabilities. Methods: An exploratory qualitative study using qualitative content analysis was conducted. Data were collected through three dyadic stakeholder interviews, each involving one sewing worker and one manager, and four one-on-one semi-structured interviews with vocational rehabilitation professionals. Results: The analysis identified fourteen employment-related categories, four critical work tasks, and four job demand domains relevant to sewing work. Conclusion: This study identified employment-related categories, critical work tasks, and job demand domains in disability-inclusive sewing employment in China. These findings may inform future vocational evaluation and AI-enhanced job analysis, with video-based analysis positioned as a complementary tool for quantifying observable physical job demands.

Indexed as

employmentjob analysissewingvocational evaluationwork ability

Identifiers

PMID42494856
PMCPMC13391579

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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.