Evidence map›Paper›PMID 39679173›Full record

ArticleFrontiers in veterinary science2024

Evaluation and spatiotemporal evolution of veterinary talent competitiveness: a new perspective of veterinary education.

Xianhang Xu, Mohd Anuar Arshad, Yugang Jian, Arshad Mahmood, Mengdie Dong

Abstract read
In one paragraph

Article in Frontiers in veterinary science, 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

5 authors.

Xianhang XuSchool of Management, Chongqing Institute of Engineering, Chongqing, China.
Mohd Anuar ArshadSchool of Management, Universiti Sains Malaysia, Penang, Malaysia.
Yugang JianSchool of Management, Chongqing Institute of Engineering, Chongqing, China.
Arshad MahmoodSchool of Business and Economics, University of Wuppertal, Wuppertal, Germany.
Mengdie DongSchool of Management, Chongqing Institute of Engineering, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Improving the quantity and quality of veterinary talent cultivation is an urgent issue to address in current veterinary education and is key to enhancing veterinary talent competitiveness. Starting from an industrial perspective, the introduction of scientific analytical methods for studying veterinary talent competitiveness offers a new view on veterinary education and helps to improve the quality of veterinary education and talent cultivation. Methods: This study develops a veterinary talent competitiveness evaluation index based on the characteristics of veterinary talent. It proposes a visual method to analyze the level and spatiotemporal evolution of veterinary talent competitiveness using the Entropy method and ArcGIS tools, with Western China as a case study. Data is collected from 12 regions in Western China, spanning 2015 to 2021. Results: The results show that the method not only evaluates the current state of veterinary talent competitiveness but also considers temporal and spatial evolution, achieving good evaluation effectiveness and high accuracy, thereby guiding the improvement of veterinary education and talent cultivation. Discussion: Based on the research findings, the study suggests improving the quality of veterinary education and talent cultivation through measures such as strengthening talent resource development, addressing regional imbalances, and promoting spatial integration to achieve a virtuous cycle between veterinary education and industrial development.

Indexed as

evaluation indexspatiotemporal evolutiontalent competitivenessveterinaryveterinary education

Identifiers

PMID39679173
PMCPMC11638244

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.