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Record W4224129051 · doi:10.2196/33360

Learning Agility of Learning and Development Professionals in the Life Sciences Field During the COVID-19 Pandemic: Empirical Study

2022· article· en· W4224129051 on OpenAlex
Xinyun Peng, Nicole Wang, William Magagna, Susan M. Land, Kyle L. Peck

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInteractive Journal of Medical Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCollaboration in agile enterprises
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicWork (physics)Coronavirus disease 2019 (COVID-19)Face (sociological concept)PsychologyPublic relationsPolitical scienceBusinessMedical educationSociologyMedicineEngineeringSocial science

Abstract

fetched live from OpenAlex

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.

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.

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)0.026
metaresearch head score (Gemma)0.047
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.166
GPT teacher head0.511
Teacher spread0.344 · 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