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Record W150101024 · doi:10.2196/med20.4327

Harnessing the Web: How E-Health and E-Health Literacy Impact Young Adults’ Perceptions of Online Health Information

2015· article· en· W150101024 on OpenAlexvenueno aff
Rowena Briones

Bibliographic record

VenueMedicine 2 0 · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsHealth literacyMeaning (existential)Grounded theoryPsychologyThe InternetHealth informationInternet privacyHealth educationModerationConstruct (python library)Health communicationInformation seekingQualitative researchMedical educationSocial psychologyWorld Wide WebComputer scienceSociologyMedicineHealth carePublic healthNursingPolitical scienceCommunication

Abstract

fetched live from OpenAlex

BACKGROUND: The rise of technology has changed how people take control of their health, enabling individuals to choose to live healthier lives and make better treatment decisions. With this said, the Internet has emerged as the channel used by individuals for actively seeking or passively receiving health information. OBJECTIVE: To explore how young adults assess the quality of health information, and how they construct meaning of online health information in general. Through 50 in-depth interviews, this study aims to examine how and why young adults turn to the Web for health information, and what strategies they employ to ensure that they are getting credible information. METHODS: A total of 50 in-depth interviews were conducted with young adults to explore how they make meaning of online health information. Depending on the geographic area of the participant, the interview took place face-to-face at a location convenient for them, over Skype, or over the telephone and lasted on average 40 minutes. The interviews were transcribed verbatim, fully retaining the speech style of the moderator and the participants. Data were analyzed using techniques from the grounded theory approach, using a constant comparative method to allow for themes to emerge from the transcripts. RESULTS: The participants shared several benefits to this mode of health information seeking, claiming that it made for more productive visits with doctors and made health information more readily accessible through a variety of different formats. Additionally, the participants demonstrated their e-health literacy levels by discussing how they assessed online health information, engaging in a series of strategies that encompassed different aspects of e-health literacy. Social media channels were brought up by the participants as relatively new tools that can be used to assist in the seeking, understanding, and sharing of health information. However, participants also cautioned about the use of social media in regards to its informal nature, warning users to evaluate sources accordingly and to use these channels as supplementary outlets of information for more traditional channels. CONCLUSIONS: The use of the Internet and technology for health purposes is a growing area for both scholarship and practice that has strong implications for health consumers, medical professionals, and communicators alike. The findings that emerged from this research demonstrated that the online space is an acceptable channel through which young adults can find and share information. However, in spite of the rising usage of social media by this particular group, the findings showed that they were hesitant and wary of the channel, not seeing it as a resource for health information but more of a channel for networking and entertainment. In spite of this, this study shows that the online health information seeking behaviors is an area that warrants further exploration.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0000.000
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 head0.076
GPT teacher head0.489
Teacher spread0.414 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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