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Record W4226097184 · doi:10.1177/23743735221089459

Building Families Through Healthcare: Experiences of Lesbians Using Reproductive Services

2022· article· en· W4226097184 on OpenAlexaffabout
Kelly Gregory, John G. Mielke, Elena Neiterman

Bibliographic record

VenueJournal of Patient Experience · 2022
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLesbianTransgenderQueerFertilityQualitative researchNursingPsychologyMedicineGender studiesSociologyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

The use of assisted human reproduction (AHR) represents a meaningful and important life event for lesbians wishing to create biologically related families. Despite increasing numbers of lesbians utilizing AHR services, barriers to access persist. This qualitative study investigated the experiences of lesbians and their interactions with reproductive services in Ontario, Canada, where limited public funding is available for all AHR patients and where the lesbian, gay, bisexual, transgender, and queer (LGBTQ) community makes up to 30% of clientele. Eleven semi-structured interviews were conducted, and findings revealed a wide range of experiences. Lesbian patients expressed a desire for more support from their care providers in navigating a complex and costly medical journey through a system largely designed for the needs of heterosexual patients. Additionally, private fertility clinics, as the environment for accessing publicly funded services, were felt to contribute pressure to pay out-of-pocket for add-on medical procedures. To improve the quality of care, participants recommended providing more high-level information on the medical journey and taking an individual approach with lesbian patients, in particular, assuming a patient has sufficient fertility until proven otherwise.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.104
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.049
GPT teacher head0.363
Teacher spread0.315 · 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 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

Citations11
Published2022
Admission routes2
Has abstractyes

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