How Adolescents Use Technology for Health Information: Implications for Health Professionals from Focus Group Studies
Bibliographic record
Abstract
BACKGROUND: Adolescents present many challenges in providing them effective preventive services and health care. Yet, they are typically the early adopters of new technology (eg, the Internet). This creates important opportunities for engaging youths via eHealth. OBJECTIVE: To describe how adolescents use technology for their health-information needs, identify the challenges they face, and highlight some emerging roles of health professionals regarding eHealth services for adolescents. METHODS: Using an inductive qualitative research design, 27 focus groups were conducted in Ontario, Canada. The 210 participants (55% female, 45% male; median age 16 years) were selected to reflect diversity in age, sex, geographic location, cultural identity, and risk. An 8-person team analyzed and coded the data according to major themes. RESULTS: Study participants most-frequently sought or distributed information related to school (89%), interacting with friends (85%), social concerns (85%), specific medical conditions (67%), body image and nutrition (63%), violence and personal safety (59%), and sexual health (56%). Finding personally-relevant, high-quality information was a pivotal challenge that has ramifications on the depth and types of information that adolescents can find to answer their health questions. Privacy in accessing information technology was a second key challenge. Participants reported using technologies that clustered into 4 domains along a continuum from highly-interactive to fixed information sources: (1) personal communication: telephone, cell phone, and pager; (2) social communication: e-mail, instant messaging, chat, and bulletin boards; (3) interactive environments: Web sites, search engines, and computers; and (4) unidirectional sources: television, radio, and print. Three emerging roles for health professionals in eHealth include: (1) providing an interface for adolescents with technology and assisting them in finding pertinent information sources; (2) enhancing connection to youths by extending ways and times when practitioners are available; and (3) fostering critical appraisal skills among youths for evaluating the quality of health information. CONCLUSIONS: This study helps illuminate adolescent health-information needs, their use of information technologies, and emerging roles for health professionals. The findings can inform the design and more-effective use of eHealth applications for adolescent populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.108 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".