MétaCan
Menu
Back to cohort
Record W2074782377 · doi:10.2196/jmir.3588

Patient Perspectives on Online Health Information and Communication With Doctors: A Qualitative Study of Patients 50 Years Old and Over

2015· article· en· W2074782377 on OpenAlexafffund
Michelle Pannor Silver

Bibliographic record

VenueJournal of Medical Internet Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of TorontoThe Scarborough Hospital
FundersUniversity of Toronto ScarboroughUniversity of Toronto
KeywordsQualitative researchMedicineHealth communicationPatient portalHealth carePsychologyFamily medicineMedical educationInternet privacyComputer scienceCommunicationSociology

Abstract

fetched live from OpenAlex

BACKGROUND: As health care systems around the world shift toward models that emphasize self-care management, there is increasing pressure for patients to obtain health information online. It is critical that patients are able to identify potential problems with using the Internet to diagnose and treat a health issue and that they feel comfortable communicating with their doctor about the health information they acquire from the Internet. OBJECTIVE: Our aim was to examine patient-identified (1) problems with using the Internet to identify and treat a health issue, (2) barriers to communication with a doctor about online health information seeking, and (3) facilitators of communication with a doctor about patient searches for health information on the Internet. METHODS: For this qualitative exploratory study, semistructured interviews were conducted with a sample of 56 adults age 50 years old and over. General concerns regarding use of the Internet to diagnose and treat a health issue were examined separately for participants based on whether they had ever discussed health information obtained through the Internet with a doctor. Discussions about barriers to and facilitators of communication about patient searches for health information on the Internet with a doctor were analyzed using thematic analysis. RESULTS: Six higher-level general concerns emerged: (1) limitations in own ability, (2) credibility/limitations of online information, (3) anxiety, (4) time consumption, (5) conflict, and (6) non-physical harm. The most prevalent concern raised by participants who communicated with a doctor about their online health information seeking related to the credibility or limitations in online information. Participants who had never communicated with a doctor about their online health information seeking most commonly reported concerns about non-physical harm. Four barriers to communication emerged: (1) concerns about embarrassment, (2) concerns that the doctor doesn't want to hear about it, (3) belief that there is no need to bring it up, and (4) forgetting to bring it up. Facilitators of communication included: (1) having a family member present at doctor visits, (2) doctor-initiated inquiries, and (3) encountering an advertisement that suggested talking with a doctor. CONCLUSIONS: Overall, participants displayed awareness of potential problems related to online health information seeking. Findings from this study point to a set of barriers as well as facilitators of communication about online health information seeking between patients and doctors. This study highlights the need for enhanced patient communication skills, eHealth literacy assessments that are accompanied by targeted resources pointing individuals to high-quality credible online health information, and the need to remind patients of the importance of consulting a medical professional when they use online health resources to diagnose and treat a health issue.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0070.005
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.167
GPT teacher head0.593
Teacher spread0.426 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations258
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Medical Internet ResearchSame topicHealth Literacy and Information AccessibilityFrench-language works237,207