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Record W4388617183 · doi:10.1186/s12905-023-02743-z

Patients’ and providers’ perspectives on the decision to undergo non-urgent egg freezing: a needs assessment

2023· article· en· W4388617183 on OpenAlexaffabout
E. Shirin Dason, Leah Drost, Ellen Greenblatt, Adena Scheer, Jinglan Han, Tanya Doshi, Claire Jones

Bibliographic record

VenueBMC Women s Health · 2023
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsUniversity of AlbertaUniversity of TorontoUniversity Health NetworkSt. Michael's HospitalSinai Health SystemMount Sinai Hospital
Fundersnot available
KeywordsRegretPsychosocialQualitative researchPerceptionDecision support systemFertility preservationPsychologyFamily medicineMedicineNursingFertilityPopulationPsychiatryComputer scienceData mining

Abstract

fetched live from OpenAlex

BACKGROUND: Previous research has demonstrated that patients have difficulty with the decision to undergo non-urgent egg freezing (EF). This study aimed to investigate the decisional difficulties and possible decisional support mechanisms for patients considering EF, and for their providers. METHODS: This qualitative study involved a needs assessment via individual interviews. Participants included patients considering EF at one academic fertility clinic and providers from across Canada who counsel patients considering EF. 25 participants were included (13 providers and 12 patients). The interview guide was developed according to the Ottawa Decision Support Framework. Interviews were transcribed, and transcripts analyzed for themes and concepts using NVIVO 12. FINDINGS: Multiple factors contributing to decisional difficulty were identified, including: (1) multiple reproductive options available with differing views from patients/providers regarding their importance; (2) a decision typically made under the pressure of reproductive aging; (3) uncertainty surrounding the technology/inadequate outcome data; (4) the financial burden of EF; (5) inherent uncertainty relating to potential decision regret; and (6) differing perceptions between patients/providers regarding the role providers should play in the decision. Additionally, potential sources of decisional support were identified, including provision of basic information before and/or during initial consultation, followed by an opportunity during or after initial consultation for clarifying information and helping with value judgements. Individualized counselling based on patient values, adequate follow-up, psychosocial counselling, and peer support were also emphasized. CONCLUSIONS: More decisional support for women considering EF is needed. Suggestions include a patient decision aid in conjunction with modified healthcare provider counselling, support and follow up.

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)0.019
metaresearch head score (Gemma)0.037
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.349
Teacher spread0.302 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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