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Record W1968588668 · doi:10.2196/med20.1996

Physicians' Motives for Professional Internet Use and Differences in Attitudes Toward the Internet-Informed Patient, Physician–Patient Communication, and Prescribing Behavior

2012· article· en· W1968588668 on OpenAlexvenueno aff
Martina Moick, Ralf Terlutter

Bibliographic record

VenueMedicine 2 0 · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetPsychologyMedicineFamily medicineMedical educationInternet privacyWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Physicians have differing motives for using the Internet and Internet-related services in their professional work. These motives may affect their evaluation of patients who bring with them health-related information from the Internet. Differing motives may also affect physician-patient communication and subsequent prescribing behavior. OBJECTIVES: to segment physicians into types based on their motives for using the internet in connection with professional activities and to analyze how those segments differ in their attitudes in three areas: toward patients who bring along Internet-sourced information; in their own subsequent prescribing behavior; and in their attitudes toward using the Internet to communicate with patients in future. METHODS: We surveyed 287 German physicians online from three medical fields. To assess physicians' motives for using the Internet for their professional activities, we asked them to rate their level of agreement with statements on a 7-point scale. Motive statements were reduced to motive dimensions using principal component analysis, and 2-step cluster analysis based on motive dimensions identified different segments of physicians. Several statements assessed agreement or disagreement on a 7-point scale physicians' attitudes toward patients' bringing Internet information to the consultation and their own subsequent prescribing behavior. Further, we asked physicians to indicate on a 7-point scale their valuation of the Internet for physician-patient communication in the future. Data were then subjected to variance and contingency analyses. RESULTS: we identified three motive dimensions for internet use: (1) being on the cutting edge and for self-expression (Cronbach alpha = .88), (2) efficiency and effectiveness (alpha = .79), and (3) diversity and convenience (alpha = .71). These three factors accounted for 71.4% of the variance. Based on physicians' motives for using the Internet, four types of physician Internet user were identified: (1) the Internet Advocate (2), Efficiency-Oriented, (3) Internet Critic, and (4) Driven Self-expressionist. Groups differed significantly concerning (1) their attitude toward informed patients in general (F 1234 = 9.215, P < .001), (2) perceived improvement in the physician-patient relationship Internet information brings (F 1234 = 5.386, P < .001), (3) perceived accuracy of information the patient brings (F 1234 = 3.658, P = .01), and (4) perceived amount of time needed to devote to an Internet-informed patient (F 1234 = 3.356, P = .02). Physician segments did not differ significantly in reported prescribing behavior (F 1234 = 1.910, P = .13). However, attitudes toward using the Internet to communicate with patients in future differed significantly (F 1234 = 23.242, P < .001). CONCLUSIONS: Based on self-reporting by German physicians of their motives for professional Internet use, we identified four types of Internet users who differ significantly in their attitude toward patients who bring along Internet information and their attitudes toward using the Internet to communicate with patients in future.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.135
GPT teacher head0.438
Teacher spread0.303 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations39
Published2012
Admission routes1
Has abstractyes

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