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Record W4283763735 · doi:10.1093/humrep/deac107.681

P-735 Exploring representation and inclusivity in fertility and reproductive health societies’ leadership

2022· article· en· W4283763735 on OpenAlexaboutno aff
Nafisat Ohunene Usman, Bola Grace

Bibliographic record

VenueHuman Reproduction · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsFertilityDiversity (politics)Reproductive healthReproductive medicineEthnic groupPolitical scienceGender diversityPublic healthCorporate governanceGender studiesSociologyDemographyMedicinePopulationLawManagementBiologyNursingPregnancy

Abstract

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Abstract Study question How diverse is the board-level executive leadership of the leading fertility and reproductive health societies in Europe, Australia and North America? Summary answer There is good gender diversity among the reproductive health societies included in the study, with limited ethnic diversity. What is known already Reproductive health societies promote understanding and interest in reproductive biology and medicine. They are leading authorities, providing guidelines, opinions, and direction to practitioners, policy makers and the public. Membership on these societies’ board is a marker of influence and prestige. Many societies have clear Equality and Diversity Statement on their website, suggesting that they value representation of members from whom they obtain fees. This study presents a quantification of the executive leadership demographic diversity of major fertility and reproductive health societies in Europe, Australia and North America to evaluate diversity in governance. Study design, size, duration We conducted a review of the websites of ten leading fertility and reproductive health societies in the Europe, Australia and North America to quantity gender and ethnic diversity. Data analysis was conducted on the information obtained in January 2022. We included the executive leadership team /governing board members but excluded subgroup leaders or special interest group coordinators. Participants/materials, setting, methods Organisations reviewed include: American College of Obstetricians and Gynaecologists(ACOG), American Society for Reproductive Medicine(ASRM), British Fertility Society(BFS), Canadian Fertility and Andrology Society(CFAS), European Society of Human Reproduction and Embryology(ESHRE), The Fertility Society of Australia and New Zealand(FSA), International Federation of Obstetrics and Gynaecology(FIGO), The Royal Australian and New Zealand College of Obstetricians and Gynaecologists(RANZCOG), Royal College of Obstetricians and Gynaecologists (RCOG), and The Society of Obstetricians and Gynaecologists of Canada(SOGC). Main results and the role of chance Proportion for each demographic group at the time of the study are summarised below; where n = total number of board members; Gender: W= women, M= Men; ethnicity: Wh = White, B = Black and A = Asian. In total, the number of board level/executive leadership members, responsible for governance in the societies reviewed were 112. Gender diversity was 41% Men, 59% Women, while ethnic diversity was 82% White, 3% Black and 15% Asian. It is encouraging to see the gender parity in the executive leadership of the organisations review, there remains an important need to improve ethnic diversity in order to better represent the membership and wider community they serve. This has implications for role-modelling, equity, minimising the negative impact of groupthink and reaching/giving underrepresented group a voice. Limitations, reasons for caution Results presented are based on a snapshot at the time of review. Organisations periodically change leadership. Additionally, gender identification is based on self-identification in individual’s profile biography. Wider implications of the findings As reproductive health organisations continue make extensive contributions to the field, it is important for their leadership to represent the diversity of members and wider population they serve. There remains a need to move beyond diversity and equality statement to actively deploy policies and processes to improve and monitor representation. Trial registration number not applicable

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.352
GPT teacher head0.380
Teacher spread0.028 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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