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Record W4360838312 · doi:10.1016/j.heliyon.2023.e14867

Hormonal contraception and thrombosis: Identifying the gaps in knowledge among females in post-secondary education

2023· article· en· W4360838312 on OpenAlexaffabout
Skylar Tierney, Yan Deng, Alysha Geauvreau, Natalie Kearn, Jessica Hodgson, Maha Othman

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsSt. Lawrence CollegeQueen's University
Fundersnot available
KeywordsPost menopausalThrombosisMedicineHormonal contraceptionGynecologyFamily planningObstetricsFamily medicinePhysiologyInternal medicinePopulationResearch methodologyEnvironmental health

Abstract

fetched live from OpenAlex

Objective: To determine the current level of knowledge about hormonal contraception among young women so they may be better informed about the risks and various choices available to them regarding hormonal contraception (HC). Methods: In an online survey-based study, data was analyzed from the anonymous responses of 675 female participants aged 18-30 years in various academic programs at two post-secondary institutions in Kingston, Ontario. Surveys explored demographics, use/type/duration of hormonal contraception, and knowledge of HC and thrombosis. Kruskal Wallis test and Spearman Correlation were used to determine differences in knowledge level about contraceptives across age groups, education levels, as well as use/type/duration of HC. Results: 476 participants were users of HC (264 > 1 year) and 199 were non-HC users. 370 participants have a high school diploma. The knowledge level of HC risks was associated with duration of use and overall knowledge of thrombosis and HC. The knowledge level of thrombosis was correlated with duration of use, education level, and age. Participants with higher level of education or those that have been using HC for 5 years or longer had an increased knowledge surrounding thrombosis. Participants aged 24 and older had a higher knowledge of thrombosis than that of participants younger than 24. Overall, the data was used to generate a simple infographic to further educate women in this regard. Conclusion: Misconceptions remain among young women concerning benefits and risks of HC which can be addressed by formal education.

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)0.000
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 score0.206
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.042
GPT teacher head0.355
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 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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