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Record W4404841236 · doi:10.32920/27931875

What Are the Real-World Podcast-Listening Habits of Medical Professionals?

2024· preprint· en· W4404841236 on OpenAlexaff
Damian Roland, Brent Thoma, Andrew Tagg, Jason Woods, Teresa M. Chan, Jeff Riddell

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicRadio, Podcasts, and Digital Media
Canadian institutionsMcMaster UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsActive listeningBusinessPsychologyCommunication

Abstract

fetched live from OpenAlex

<p>Introduction Educational podcasts are increasingly being utilized by health professionals for continuing education, but how they are being used remains poorly understood. Given their extensive reach, they represent a phenomenal opportunity for researchers to engage in knowledge translation of their scholarly works. The design, study, and effectiveness of these resources should be informed by a deeper understanding of their pragmatic usage. We aimed to prospectively determine the pragmatic, real-world listening habits of health professionals. Methods We performed a prospective observational study of a broad, interprofessional sample of participants (medical students, residents, physicians, nurses, physician assistants, and paramedics) recruited through a multimodal social media (Twitter and Facebook) campaign. Recruitment materials included an infographic and study website. Participants listened to eight podcasts and described their use of each in subsequent questionnaires. Results A total of 393 participants enrolled in the study, and 241 completed the survey for all eight podcasts. Listening behaviors were consistent across the podcasts with the majority selecting a normal speed of playback and engaging in concomitant activities such as driving. One-third of participants paused the podcasts due to interruption. Conclusion We describe the prospective use of medical podcasts by a cohort of health professionals. This work should inform the role of podcasts in the communication of medical research.</p>

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
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 head0.053
GPT teacher head0.386
Teacher spread0.333 · 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 designTheoretical or conceptual
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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