MétaCan
Menu
Back to cohort
Record W2901458340 · doi:10.1016/j.rbms.2018.10.016

Infertility patients' need and preferences for online peer support

2018· article· en· W2901458340 on OpenAlexafffund
Paul H. Grunberg, Cindy‐Lee Dennis, Deborah Da Costa, Phyllis Zelkowitz

Bibliographic record

VenueReproductive Biomedicine & Society Online · 2018
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsMcGill University Health CentreUniversity of TorontoJewish General HospitalMcGill University
FundersCanadian Institutes of Health Research
KeywordsFertilityInfertilityPeer supportFertility clinicDemographyDemographicsReproductive medicineMedicineEthnic groupPsychologyFamily medicineSocial psychologyPopulationPregnancyNursingEnvironmental health

Abstract

fetched live from OpenAlex

= 519) seeking fertility treatment were recruited from four clinics in Ontario and Quebec, Canada. Participants completed an anonymous online questionnaire assessing demographics, perceived stress and fertility characteristics, in addition to interest in and preferences for online infertility peer support. Most men (80.1%) and women (89.8%) expressed interest in online peer support, with perceived stress being related to interest among both men and women. Non-White ethnicity and lower income were related to greater interest among men. Patients reported a preference for mobile accessibility, monitored peer-to-peer communication, and links to information. Men and women, particularly those with high levels of perceived stress, expressed interest in online peer support and shared similar preferences for features irrespective of fertility characteristics. Demographic characteristics and perceived stress were related to a desire for more personalized support options.

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)0.013
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.048
GPT teacher head0.361
Teacher spread0.313 · 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

Citations31
Published2018
Admission routes2
Has abstractno

Explore more

Same venueReproductive Biomedicine & Society OnlineSame topicReproductive Health and TechnologiesFrench-language works237,207