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Record W4399545883 · doi:10.5114/hpr/187336

Researching researchers: exploring the challenges of conducting research during a pandemic

2024· article· en· W4399545883 on OpenAlexaff
O Onuoha, Diane Lorenzetti, Jacqueline Reynolds Pearson, Bernice Lee, Tanya Beran

Bibliographic record

VenueHealth Psychology Report · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of AlbertaAlberta HealthUniversity of Calgary
Fundersnot available
KeywordsCreativityThematic analysisTheme (computing)PandemicPsychologyData collectionCoronavirus disease 2019 (COVID-19)Qualitative researchInterpersonal communicationMedical educationSociologyMedicineSocial psychologyDiseaseSocial scienceComputer science

Abstract

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BACKGROUND: Research assistants (RAs) are vital for the successful completion of research. When data collection and recruitment are disrupted, like during the COVID-19 pandemic and accompanying restrictions, the effects on RAs attempting to conduct research are unclear. PARTICIPANTS AND PROCEDURE: could help patients uphold health and safety procedures during the COVID-19 pandemic participated in semi-structured interviews. RESULTS: Thematic analysis of the interview data identified four key themes (and sub-themes) that reflected RAs' experiences of conducting research during the COVID-19 pandemic: inspiration and motivation; research barriers; human connections and relationships; and creativity and problem-solving. The first theme focused on the sources of RAs' inspiration and motivation to participate in research; the second focused on the barriers that affected data collection and recruitment. The third theme described the impact that human connections and relationships had on the success of the research, and the final theme explored the RAs' creativity and problem-solving approaches, which aided in navigating the challenges faced during the pandemic. The RAs overcame the challenges with positive attitudes, creativity, and collaboration. CONCLUSIONS: Overall, the results reveal how the RAs explored creative strategies to adapt research methods to suit unanticipated circumstances and develop interpersonal skills to facilitate participation in future research and career activities.

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)0.079
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0790.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.006
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 head0.939
GPT teacher head0.739
Teacher spread0.201 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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