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The Development of an International Oncofertility Competency Framework: A Model to Increase Oncofertility Implementation

2019· article· en· W2947039620 on OpenAlexaff
Antoinette Anazodo, Paula Laws, Shanna Logan, Carla Saunders, Joanne Travaglia, Brigitte Gerstl, Natalie Bradford, Richard J. Cohn, Mary Birdsall, Ronald D. Barr, Nao Suzuki, Seido Takae, Ricardo Marinho, Shuo Xiao, Qiong-Hua Chen, Nalini Mahajan, Madhuri Patil, Devika Gunasheela, Kristen Smith, Leonard S. Sender, Cláudia Melo, Teresa Almeida‐Santos, Mahmoud Salama, Leslie Appiah, Irene Su, Sheila Lane, Teresa K. Woodruff, Allan Pacey, Richard A. Anderson, Françoise Shenfield, Elizabeth Sullivan, William J. Ledger

Bibliographic record

VenueThe Oncologist · 2019
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOncofertilityMedicineFertility preservationPsychosocialDelphi methodFamily medicineReferralSurvivorship curveService (business)DocumentationNursingFertilityCancerInternal medicinePopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Despite international evidence about fertility preservation (FP), several barriers still prevent the implementation of equitable FP practice. Currently, oncofertility competencies do not exist. The aim of this study was to develop an oncofertility competency framework that defines the key components of oncofertility care, develops a model for prioritizing service development, and defines the roles that health care professionals (HCPs) play. MATERIALS AND METHOD: A quantitative modified Delphi methodology was used to conduct two rounds of an electronic survey, querying and synthesizing opinions about statements regarding oncofertility care with HCPs and patient and family advocacy groups (PFAs) from 16 countries (12 high and 4 middle income). Statements included the roles of HCPs and priorities for service development care across ten domains (communication, oncofertility decision aids, age-appropriate care, referral pathways, documentation, oncofertility training, reproductive survivorship care and fertility-related psychosocial support, supportive care, and ethical frameworks) that represent 33 different elements of care. RESULTS: The first questionnaire was completed by 457 participants (332 HCPs and 125 PFAs). One hundred and thirty-eight participants completed the second questionnaire (122 HCPs and 16 PFAs). Consensus was agreed on 108 oncofertility competencies and the roles HCPs should play in oncofertility care. A three-tier service development model is proposed, with gradual implementation of different components of care. A total of 92.8% of the 108 agreed competencies also had agreement between high and middle income participants. CONCLUSION: FP guidelines establish best practice but do not consider the skills and requirements to implement these guidelines. The competency framework gives HCPs and services a structure for the training of HCPs and implementation of care, as well as defining a model for prioritizing oncofertility service development. IMPLICATIONS FOR PRACTICE: Despite international evidence about fertility preservation (FP), several barriers still prevent the implementation of equitable FP practice. The competency framework gives 108 competencies that will allow health care professionals (HCPs) and services a structure for the development of oncofertility care, as well as define the role HCPs play to provide care and support. The framework also proposes a three-tier oncofertility service development model which prioritizes the development of components of oncofertility care into essential, enhanced, and expert services, giving clear recommendations for service development. The competency framework will enhance the implementation of FP guidelines, improving the equitable access to medical and psychological oncofertility care.

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)0.003
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 score0.341
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.054
GPT teacher head0.402
Teacher spread0.348 · 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

Citations42
Published2019
Admission routes1
Has abstractyes

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