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Record W2158862092 · doi:10.1177/1524839904263897

Online Discussions With Pregnant and Parenting Adolescents: Perspectives and Possibilities

2005· review· en· W2158862092 on OpenAlexaff
Ruta Valaitis, Wendy Sword

Bibliographic record

VenueHealth Promotion Practice · 2005
Typereview
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsHamilton Health SciencesMcMaster University
Fundersnot available
KeywordsPsychologyThe InternetQualitative researchOnline participationMedical educationSocial supportPopulationService providerPublic relationsInternet privacyService (business)Social psychologyMedicineSociologyWorld Wide WebComputer scienceBusinessPolitical science

Abstract

fetched live from OpenAlex

The Internet is an innovative strategy to increase public participation. It is important to include pregnant and parenting teens' perspectives when planning programs to meet their needs. This qualitative study explored online discussions as a strategy to enhance participation by this population. Findings showed that online communication was preferred over face-to-face group discussions. Being anonymous online encouraged open and honest feedback. Participants experienced various forms of social support, however, there was an overall lack of teen involvement online. Strategies to engage adolescents in online discussions and reduce barriers are discussed. Strategies included the use of teen moderators, home computer access, technical support, and engagement in naturally flowing online discussions to meet social support needs. Blending researchers' with teens' needs for social support in an online environment is encouraged. With careful planning and design, online communications can result in mutual benefits for researchers, service providers, and pregnant and parenting adolescents.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.130
GPT teacher head0.488
Teacher spread0.358 · 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
GenreReview

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

Citations29
Published2005
Admission routes1
Has abstractyes

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