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Record W4386523774 · doi:10.1371/journal.pone.0288010

Using refined methods to pick up mice: A survey benchmarking prevalence & beliefs about tunnel and cup handling

2023· article· en· W4386523774 on OpenAlexaff
Lauren Young, Donna Goldsteen, Elizabeth A. Nunamaker, Mark J. Prescott, Penny S. Reynolds, Sally Thompson-Iritani, Sarah E Thurston, Tara Martin, Megan R. LaFollette

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsControl (management)Theory of planned behaviorWelfareBenchmarkingPsychologyNormativeMedicineApplied psychologyComputer scienceSocial psychologyMarketingArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

Refined handling improves laboratory mouse welfare and research outcomes when compared to traditional tail handling, yet implementation does not seem to be widespread. Refined handling includes picking up a mouse using a tunnel or cupped hands. The aim of this study was to determine the current prevalence of and beliefs towards refined handling using the theory of planned behavior. It was predicted that refined handling prevalence is low compared to traditional handling methods, and its implementation is determined by individual and institutional beliefs. Research personnel were recruited via online convenience sampling through email listservs and social media. A total of 261 participants in diverse roles (e.g. veterinarians, managers, caretakers, researchers, etc.) responded primarily from the USA (79%) and academic institutions (61%) Participants were surveyed about their current use, knowledge, and beliefs about refined handling. Quantitative data were analyzed via descriptive statistics and generalised regression. Qualitative data were analyzed by theme. Research personnel reported low levels of refined handling implementation, with only 10% of participants using it exclusively and a median estimate of only 10% of institutional mice being handled with refined methods. Individually, participants had positive attitudes, neutral norms, and positive control beliefs about refined handling. Participants' intention to provide refined handling in the future was strongly associated with their attitudes, norms, and control beliefs (p<0.01). Participants believed barriers included jumpy mice, perceived incompatibility with restraint, lack of time, and other personnel. However, participants also believed refined handling was advantageous to mouse welfare, handling ease, personnel, and research. Although results from this survey indicate that current refined handling prevalence is low in this sample, personnel believe it has important benefits, and future use is associated with their beliefs about the practice. People who believed refined handling was good, felt pressure to use it, and were confident in their use reported higher implementation. Increased refined handling could be encouraged through education on misconceptions, highlighting advantages, and addressing important barriers.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.613
GPT teacher head0.488
Teacher spread0.125 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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