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Record W4379793110 · doi:10.1080/23293691.2023.2215765

Building Social Support: Disclosure and Communication Processes Between IVF Patients and Peers in Canada

2023· article· en· W4379793110 on OpenAlexaffabout
Natalie Dimitra Montgomery, Jenepher Lennox Terrion, Eric Crighton

Bibliographic record

VenueWomen s Reproductive Health · 2023
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutions3v Geomatics (Canada)University of OttawaPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsPsychologyInternet privacyBusinessSocial psychologyComputer science

Abstract

fetched live from OpenAlex

Infertility, and the choice to attempt assisted reproductive technology, is the source of significant stress for patients pursuing in vitro fertilization (IVF), and this compels many to identify and leverage psychosocial supports. Because the quality of social support individuals receive depends on the nature of the communication they share with the receiver, it is important to consider how disclosure builds social support. We explored the IVF patient and peer communication process and the disclosure of fertility-related and non-fertility-related information by conducting 23 interviews with first-time and recurring IVF patients. Results show that IVF patients share natural, immediate, and backward disclosure transitions; share a mutual understanding of engagement boundaries; have a propensity for reciprocal sharing; and prefer digital communication for their interactions. While participants reported disclosing a wide range of aspects of their condition and its treatment, such as treatment protocol, diagnosis/IVF attempts, medication and injections, financial questions, marital adjustment, family and social acceptance, emotional adjustment, and treatment milestones, they also reported a tendency to distance themselves during the post–embryo transfer waiting period and avoided sharing other aspects of their lives. Future support strategies should frame patient–peer support as a pragmatic channel that can adapt depending on disclosure and communication preferences of patients.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.092
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.318
Teacher spread0.292 · 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.

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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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