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Record W4288691794 · doi:10.3389/fvets.2022.888189

Mental well-being and diversity, equity, and inclusiveness in the veterinary profession: Pathways to a more resilient profession

2022· article· en· W4288691794 on OpenAlexaff
Florentine Scilla Louise Timmenga, Wiebke Jansen, Patricia V. Turner, Nancy De Briyne

Bibliographic record

VenueFrontiers in Veterinary Science · 2022
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsUniversity of Guelph
FundersZoetis
KeywordsSnowball samplingEquity (law)Diversity (politics)MedicineMedical educationVeterinary medicinePolitical science

Abstract

fetched live from OpenAlex

Mental well-being (MWB) and diversity, equity, and inclusiveness (DEI) continue to be critical within the veterinary profession but there is less information regarding how professional associations around the world tackle these issues. A mixed-method study including an international online survey in English ( n = 137 responses via snowball sampling), fourteen interviews, and two webinars was used to identify the availability and impact of MWB and DEI support programs for veterinarians. Survey results showed that more veterinary organizations designated MWB and DEI challenges (54%, n = 43/79 and 58%, n = 45/78, respectively) as a key priority area than veterinary clinics (26%, n = 15/57 and 33%, n = 19/57, respectively). Whereas, MWB support programs were available in a moderate number of mainly English-speaking countries, DEI support programs were available in only a few countries and focused primarily on specific groups, with an unknown impact due to their recent implementation. Universally, survey respondents believed activities for specific groups, such as MWB webinars, training, and awareness campaigns, as well as MWB/DEI helplines and DEI peer-to-peer support programs had a high impact (median 3.5–4/5) yet were underemployed by both veterinary organization and veterinary clinics. Further feedback from respondents during focused interviews indicated that requiring initial and continuing training as well as tailored group activities would be most beneficial to improve MWB/DEI throughout the veterinary professional career. There are many areas of the intersection between MWB and DEI that remain to be elucidated in the future studies. Having a sufficient sample size, improving accessibility, and addressing varying cultural perceptions are the main challenges, as seen in our study. To truly address MWB and DEI disparities, change is also needed in veterinary workplace culture and environment. In conclusion, raising awareness for an inclusive profession, including increasing openness and acceptance to enhance DEI and destigmatizing MWB challenges, is needed to ensure a thriving, modern veterinary profession.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.000
Scholarly communication0.0000.001
Open science0.0010.035
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.156
GPT teacher head0.460
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2022
Admission routes1
Has abstractyes

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