Evidence map›Paper›PMID 38317786›Full record

ArticleFrontiers in veterinary science2023

Assessment of community-based intervention approaches to improve the health and welfare of working donkeys in selected areas of Sidama region, Southern Ethiopia.

Aweke Yalew, Daniel Darge, Berhanu Mekibib Melake

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Article in Frontiers in veterinary science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Aweke YalewWolaita Sodo Agricultural Technical Vocational and Educational Training College, Wolaita Sodo, Ethiopia.
Daniel DargeDepartment of Veterinary Medicine, Faculty of Veterinary Medicine, Hawassa University, Hawassa, Ethiopia.
Berhanu Mekibib MelakeDepartment of Veterinary Medicine, Faculty of Veterinary Medicine, Hawassa University, Hawassa, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Although efforts have been made by certain non-governmental organizations, like the Donkey Sanctuary, SPANA (Society for the Protection of Animals Abroad), and Brooke Ethiopia, to change the attitudes and practices of donkey owners toward improving the health and welfare of working donkeys, their impact has not been assessed so far. A cross-sectional study was conducted to assess and compare donkeys' health and welfare problems in community-based intervention areas versus non-intervention areas in selected districts of Sidama regional state, Southern Ethiopia. Methods: For the animal-based welfare assessments, 200 donkeys each were selected and included from intervention and non-intervention areas. The selected donkeys were then assessed for their welfare and health status using five important parameters, namely, body condition score, presence and severity of wounds, behavior, presence and severity of lameness, and presence of other signs of illness/diseases. Results: The prevalence and severity of lameness and wounds on donkeys managed in non-intervention areas were higher than those observed in community-based intervention areas. The prevalence of lameness in the non-intervention areas (25.5%) was over two times higher than the prevalence in the intervention areas (12%). Likewise, over 37% of the donkeys in the non-intervention areas were wounded, of which 64% were suffering moderate to severe wounds. Moreover, donkeys in the intervention areas had better body condition and were alert and friendly upon human approach. There was a statistically significant difference ( Conclusion and recommendations: Based on this study's findings, the community-based intervention approach was found to improve the health and welfare of working donkeys. Therefore, comprehensive and continuous equine health and welfare promotion through community-based intervention approaches should be designed and implemented to improve the welfare of working equines in the country.

Indexed as

animal based assessmentcommunity-based interventionhealthcarewelfareworking donkeys

Identifiers

PMID38317786
PMCPMC10839042

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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.