Evidence map›Paper›PMID 42591146›Full record

ArticleFrontiers in psychology2026

Algorithmic performance appraisal intensity in hospitals: implications for professional autonomy and innovative work behavior.

Qing Yang, Shuang Li, Kai Wang, Xin Chang, Shengya Cao, Guihua Zhang

Abstract read
In one paragraph

Article in Frontiers in psychology, 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.

Qing YangChina University of Mining and Technology, Xuzhou, China.
Shuang LiChina University of Mining and Technology, Xuzhou, China.
Kai WangXuzhou Third People's Hospital, Xuzhou, China.
Xin ChangXuzhou Third People's Hospital, Xuzhou, China.
Shengya CaoXuzhou Third People's Hospital, Xuzhou, China.
Guihua ZhangXuzhou Third People's Hospital, Xuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Hospitals increasingly use algorithmic systems to monitor, compare, score, and appraise employees. Although such systems may improve consistency and efficiency, intensive use may constrain medical professionals' autonomy and their innovative behavior. This study examined the relationships among algorithmic performance appraisal intensity, perceived professional autonomy, and innovative work behavior, and tested whether algorithmic transparency moderates these relationships. Methods: Three studies were conducted. Study 1 was a between-subjects vignette experiment ( Results: In Study 1, high appraisal intensity reduced perceived professional autonomy (t(237.92) = 6.66, Discussion: Across the three studies, intensive algorithmic appraisal showed inconsistent but generally autonomy-constraining relationships, whereas professional autonomy consistently supported innovative work behavior. Transparency was positively associated with autonomy but did not reliably buffer the negative effects of appraisal intensity. Hospitals should retain contextual human judgment and treat transparency as a supportive design feature rather than assume that it offsets intensive algorithmic control.

Indexed as

algorithmic HRMalgorithmic performance appraisalalgorithmic transparencyinnovative work behaviorprofessional autonomy

Identifiers

PMID42591146
PMCPMC13461422

What OpenQuestion holds

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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.