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Record W3003426014 · doi:10.2196/16704

Factors Influencing Doctors’ Participation in the Provision of Medical Services Through Crowdsourced Health Care Information Websites: Elaboration-Likelihood Perspective Study

2020· article· en· W3003426014 on OpenAlex

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

VenueJMIR Medical Informatics · 2020
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsCrowdsourcingQuality (philosophy)Health careDiversity (politics)The InternetPerspective (graphical)Test (biology)MedicineMedical educationPsychologyWorld Wide WebComputer scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Web-based crowdsourcing promotes the goals achieved effectively by gaining solutions from public groups via the internet, and it has gained extensive attention in both business and academia. As a new mode of sourcing, crowdsourcing has been proven to improve efficiency, quality, and diversity of tasks. However, little attention has been given to crowdsourcing in the health sector. OBJECTIVE: Crowdsourced health care information websites enable patients to post their questions in the question pool, which is accessible to all doctors, and the patients wait for doctors to respond to their questions. Since the sustainable development of crowdsourced health care information websites depends on the participation of the doctors, we aimed to investigate the factors influencing doctors' participation in providing health care information in these websites from the perspective of the elaboration-likelihood model. METHODS: We collected 1524 questions with complete patient-doctor interaction processes from an online health community in China to test all the hypotheses. We divided the doctors into 2 groups based on the sequence of the answers: (1) doctor who answered the patient's question first and (2) the doctors who answered that question after the doctor who answered first. All analyses were conducted using the ordinary least squares method. RESULTS: =.418, P<.001). Second, the reward that the patient offered for the best answer showed a positive effect on doctors' participation (β=.019, P<.001). Third, the question's complexity was found to positively moderate the relationships between the ability of the first doctor who answered and the participation of the following doctors (β=.186, P=.05) and to mitigate the effect between the reward and the participation of the following doctors (β=-.003, P=.10). CONCLUSIONS: This study has both theoretical and practical contributions. Online health community managers can build effective incentive mechanisms to encourage highly competent doctors to participate in the provision of medical services in crowdsourced health care information websites and they can increase the reward incentives for each question to increase the participation of the doctors.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.004
Open science0.0020.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.362
Teacher spread0.334 · 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