Navigating challenges in applied animal behaviour and welfare research: A focus group study
Bibliographic record
Abstract
Academics in applied animal behaviour and welfare science may face challenges while working within the constraints imposed by institutional and regulatory frameworks. However, to our knowledge, no studies have attempted to describe these difficulties and whether there are differences among social-demographic groups. This study aimed to address this gap by exploring researchers’ perspectives on the challenges they face in applied animal behaviour and animal welfare across multiple countries and facilitating collaborative discussions to identify potential solutions. We recruited 47 delegates attending the 56th Congress of the International Society of Applied Ethology (ISAE) held in Tallinn, Estonia, in August 2023 to participate in focus group discussions. Participants represented 33 countries covering five continents. Of the 47 delegates, three participated twice, once as part of a pre-conference workshop and then again two days later in a workshop open to all attendees. The focus group held at the pre-conference workshop had 15 participants, and six focus groups were run during the latter workshop, each with 5–6 participants. Using a semi-structured discussion guide, participants were encouraged to discuss their challenges, proposed solutions to the identified challenges, and the role international societies could play in helping them overcome some of these challenges. All focus group discussions were audio-recorded, transcribed verbatim and subjected to reflexive thematic analyses to assess participants’ attitudes to the challenges they experience. Three themes were developed to represent the different levels regarding which participants experienced challenges: (1) the discipline of animal behaviour and welfare, (2) conducting and sharing research, and (3) researcher welfare and networking. Participants described numerous barriers hindering their research process, originating from within their academic institutions, local governments, and scientific journal publication processes but also arising from prejudice and other personal challenges. Many of the challenges identified were shared among all participants, regardless of region, although certain socio-demographic groups more frequently raised specific issues, such as the need for networking opportunities and travel barriers to attend conferences. Despite the difficulty in identifying solutions, some participants believed that collaboration among researchers from different regions could help overcome some of the regional barriers, and many participants showed willingness to collaborate as a first step to striving for solutions to the identified challenges. • Challenges arose at scientific, institutional, and researcher levels. • Particular challenges were more common among specific socio-demographic groups. • Participants viewed collaboration as a first step toward achieving solutions. • International organizations have the potential to facilitate collaborations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".