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Record W4411508437 · doi:10.3329/jmcwh.v21i1.80080

Online Health Information Seeking Behaviour and Perceived Trust on Health Communication Channels among Undergraduate Students of Bangladesh

2025· article· en· W4411508437 on OpenAlexaff
Ashiqur Rahman, Abu Sadat Mohammad Nurunnabi, Rukonuzzaman Rukon, Abid Hassan, Hafiza Sultana

Bibliographic record

VenueJournal of the Medical College for Women & Hospital · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsSocial mediaThe InternetNewspaperPsychologyHealth Information National Trends SurveyInformation seekingHealth informationMedical educationHealth communicationMedicineAdvertisingHealth carePolitical scienceBusinessWorld Wide WebLibrary scienceComputer science

Abstract

fetched live from OpenAlex

Background: Health communication channels include traditional media like television, radio and print newspapers along with online newspapers and digital social media platforms like Facebook, YouTube, Twitter (X), and Blogs. The internet has become a valuable resource for accessing health information allowing individuals to actively engage in their health-related decision making. Objective: Our study aims to observe online health information seeking behaviour and perceived trust on those online health communication channels among the undergraduate level students of Dhaka City in Bangladesh. Materials & Methods: This cross-sectional study was conducted between January and December of 2023. A total of 384 students hailing from four institutions of Dhaka city namely Jagannath University, Daffodil International University, Enam Medical College and Mandy Dental College participated in this study. A pretested questionnaire was used for data collection, which included demographic characteristics, online health information seeking behaviour and perceived trust on those online health communication channels. For measuring perceived trust on online channels, the items were adopted from the Health Information National Trends Survey (HINTS) scale. Results: The majority (63%) of the study participants were ≥21 years old; the mean age was 20.98±1.18 years. 53.4% were males and 46.6% were females; male-female ratio was 1.5:1. 52% had medical/dental background. Most of the respondents used internet for health information within the week before survey (55.5%) and preferred digital channels (89%) over broadcast channels (11%) for health information. The mean of perceived trust on online channels was observed 3.13±1.64; online newspapers were the most trusted (3.03), while Facebook was the least trusted (2.41). Younger respondents aged ≤20 years reported higher perceived trust in online health information than those aged ≥21 years (P<0.05). A higher perceived trust was also observed among female students compared to male students (P<0.05). However, correlation of educational background and institutions with recent internet use did not show any statistically significant difference among the students (P>0.05). Those who preferred digital channels exhibit much greater levels of trust on those online channels compared to those who preferred broadcast channels (P<0.05). Conclusion: Undergraduate level students regularly use the internet and prefer digital channels over broadcast channels for health information. Online newspapers are found to be the most trusted among health communication channels. J Med Coll Women Hosp.2025; 21 (1):86-94

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.401
Teacher spread0.383 · 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 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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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