Evidence map›Paper›PMID 38776300›Full record

ArticlePloS one2024

Combining sentiment analysis and text mining with content analysis of farm vet interviews on mental wellbeing in livestock practice.

Andrew J Duncan, Madeleine K Henry, Kate Lamont

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Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Andrew J DuncanNorthern Faculty, Department of Veterinary and Animal Science, Centre for Epidemiology and Planetary Health, Scotland's Rural College (SRUC), Inverness, United Kingdom.ORCID 0000-0002-7314-9629
Madeleine K HenryNorthern Faculty, Department of Veterinary and Animal Science, Centre for Epidemiology and Planetary Health, Scotland's Rural College (SRUC), Inverness, United Kingdom.ORCID 0000-0003-0629-7725
Kate LamontNorthern Faculty, Department of Veterinary and Animal Science, Centre for Epidemiology and Planetary Health, Scotland's Rural College (SRUC), Inverness, United Kingdom.ORCID 0000-0003-3258-7355

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe aim of the How Farm Vets Cope project was to co-design, with farm veterinary surgeons, a set of web-based resources to help them and others deal with the different situations that they can face. As part of the wider project, participants were recruited for one-to-one semi-structured phone interviews. These interviews focused on elements of job satisfaction and how the participants coped during periods of poor mental wellbeing or with setbacks and failure.

methodsTranscripts of these interviews were analysed using both quantitative methods of sentiment analysis and text mining, including term frequency/inverse document frequency and rapid automated keyword extraction, and qualitative content analysis. The twin aims of the analysis were identifying the important themes discussed by the participants and comparing the results of the two methods to see what differences, if any, arose.

resultsAnalysis using the afinn and nrc sentiment lexicons identified emotional themes of anticipation and trust. Rapid automated keyword extraction highlighted issues around age of vets and support, whilst using term frequency/inverse document frequency allowed for individual themes, such as religion, not present across all responses, to be identified. Content analysis supported these findings, pinpointing examples of trust around relationships with farmers and more experienced vets, along with some examples of the difference good support networks can make, particularly to younger vets.

findingsThis work has confirmed previous results in identifying the themes of trust, communication and support to be integral to the experience of practicing farm veterinary surgeons. Younger or less experienced vets recognised themselves as benefiting from further support and signposting, leading to a discussion around the preparation of veterinary students for entry into a farm animal vet practice. The two different approaches taken showed very good agreement in their results. The quantitative approaches can be scaled to allow a larger number of interviews to be utilised in studies whilst still allowing the important qualitative results to be identified.

Indexed as

Data MiningLivestockMental HealthVeterinariansAdultAnimalsFarmersFarmsFemaleHumansInterviews as TopicJob SatisfactionMaleMiddle Aged

Identifiers

PMID38776300
PMCPMC11111023

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.