Learning Agility of Learning and Development Professionals in the Life Sciences Field During the COVID-19 Pandemic: Empirical Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has impacted the life sciences field worldwide. Life sciences organizations (eg, pharmaceutical and med-tech companies) faced a rapidly increasing need for vital medical products, patient support, and vaccine development. Learning and development (L&D) departments play a crucial role in life sciences organizations as they apply learning initiatives to organizational strategy within a constantly evolving sector. During the COVID-19 pandemic, the work of L&D professionals in life sciences organizations changed profoundly during the abrupt shift to remote work, since learning and training normally occur in a face-to-face environment. Given the complex and dynamic situation of the pandemic, both individuals and organizations needed to learn quickly and apply what they learned to solve new, unprecedented problems. This situation presents an opportunity to study how characteristics of learning agility were evidenced by life sciences organizations and individual employees in the remote working mode. OBJECTIVE: In collaboration with Life Sciences Trainers & Educators Networks (LTEN), this study investigated the responses and learning agility of L&D professionals and their organizational leadership within the life sciences sector to the work changes due to the pandemic. The study answered the following questions: (1) How did L&D professionals in the life sciences sector respond to the changes in their work environment during the COVID-19 pandemic? (2) How did L&D professionals in the life sciences sector demonstrate learning agility during remote working? METHODS: We adopted a mixed methods approach that included a semistructured interview and a survey. Participants who were life sciences or health care L&D practitioners and in relevant positions were recruited via email through the LTEN and its partner pharmaceutical, biotech, or medical devices organizations. Interviews with 12 L&D professionals were conducted between June and August 2020 through phone or online conferencing, covering 22 open-ended questions to stimulate ideas that could be explored further in the survey. The semistructured interview questions were grounded in theory on learning agility. In total, 4 themes were developed from the interviews, which formed the basis for developing the survey items. The subsequent survey regarding 4 specific themes was conducted from August to October 2020 using Qualtrics. Both interview and survey data were analyzed based on a learning agility framework. RESULTS: Findings revealed generally positive organizational and individual responses toward the changes brought about by the pandemic. Results also indicated that a disruptive crisis, such as the shift from working in the office to working from home (WFH), required professionals' learning agility to both self-initiate their own learning and to support the learning agility of others in the organization. CONCLUSIONS: This study was designed to better understand education and training in the life sciences field, particularly during the unique circumstances of the global COVID-19 pandemic. We put forward several directions for future research on the learning agility of L&D professionals in life sciences organizations.
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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 | 0,006 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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 ».