Navigating challenges in applied animal behaviour and welfare research: A focus group study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,054 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,014 | 0,007 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».