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Record W6991041546

Ethically challenging situations encountered by veterinary team members

2022· dissertation· en· W6991041546 on OpenAlexaboutno aff

Bibliographic record

VenueThe Sydney eScholarship Repository (The University of Sydney) · 2022
Typedissertation
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTeamworkMoral dilemmaCompetence (human resources)NegotiationHealth carePandemicMedical ethicsQualitative research
DOInot available

Abstract

fetched live from OpenAlex

Veterinary team members (VTMs) commonly encounter ethically challenging situations (ECS). ECS can lead to moral distress & impact the safety & welfare of patients. Literature searches (1990−2020) & a focused review on advanced veterinary care (AVC) identified key ECS. A global survey of VTMs during the early COVID-19 pandemic explored frequency, stressfulness & types of ECS encountered by VTMs. Frequency increased for almost half of VTMs during the pandemic. ECS encountered by VTMs, resources used to resolve ECS, & barriers to resolution are discussed. Risk factors for experiencing increased ECS during the pandemic included being a veterinary nurse or animal health technician, working with companion animals, working in USA/Canada & having low confidence dealing with ECS in the workplace. Qualitative analysis identified key factors that may lead to or exacerbate ECS during the pandemic: communication challenges & low or no-contact euthanasia. Strategies to prevent or mitigate ECS are recommended. Access to resources (e.g. technology to facilitate telemedicine, protocols to facilitate low-contact euthanasia) are needed to prevent/mitigate ECS impacts. Ethics rounds (ER), used in medical settings, was trialed with VTMs, who completed the Euro-MCD 2.0 pre & post. The Euro-MCD evaluates outcomes of ethics rounds across domains of moral competence, moral teamwork & moral action. VTMs improved in the domains of moral competence & moral teamwork after 1 session of ER. ER has potential to improve the ability of VTMs to identify & navigate ECS & to mitigate moral distress. Recommendations: veterinary empirical ethics research to include perspectives of non-veterinarian VTMs & clients, develop a validated measurement of veterinary team member moral distress, challenge the triad of veterinary stakeholders, further evaluate & develop CESS, conduct regular surveillance of ECS & to prepare VTMs, clients, animals & other stakeholders for ECS occurring in emergencies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.099
GPT teacher head0.383
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2022
Admission routes1
Has abstractyes

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