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Record W4280597596 · doi:10.2196/36266

Sexual Health Assessment Is Vital to Whole Health Models of Care

2022· article· en· W4280597596 on OpenAlexvenueno aff
Alex Uzdavines, Drew A. Helmer, Juliette F Spelman, Kristin Mattocks, Amanda M. Johnson, John F. Chardos, Kristine E. Lynch, Michael R. Kauth

Bibliographic record

VenueJMIRx Med · 2022
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
FundersCenter for Innovations in Quality, Effectiveness and SafetyU.S. Department of Veterans Affairs
KeywordsReproductive healthHealth careTransgenderSexual orientationHuman sexualityMedicineHealth equityLesbianNursingPsychologyFamily medicinePublic healthSocial psychologyEnvironmental healthPopulationPolitical science

Abstract

fetched live from OpenAlex

Sexual health is the state of well-being regarding sexuality. Sexual health is highly valued and associated with overall health. Overall health and well-being are more than the absence of disease or dysfunction. Health care systems adopting whole health models of care need to incorporate a holistic assessment of sexual health. This includes assessing patients' sexual orientation and gender identity (SOGI). If health systems, including but not limited to the Veterans Health Administration (VHA), incorporate sexual health into whole health they could enhance preventive care, promote healthy sexual functioning, and optimize overall health and well-being. Assessing sexual health can give providers important information about a patient's health, well-being, and health goals. Sexual concerns or dysfunction may also signal undiagnosed health conditions. Additionally, collecting SOGI information as part of a sexual health assessment would allow providers to address problems that drive disparities for lesbian, gay, bisexual, transgender, queer, and similar minority (LGBTQ+) populations. Health care providers do not routinely assess sexual health in clinical practice. One barrier is a gap in communication between patients and providers. Providers cite beliefs that patients will bring up sexual concerns themselves or might be offended by discussing sexual health. Patients often report an expectation that providers will bring up sexual health and being comfortable discussing sexual health with their providers. Within the VHA, the lack of a sexual health template within the electronic health record (EHR) adds an additional barrier. The VHA's transition toward whole health and updates to its EHR provide unique opportunities to integrate sexual health assessment into routine care. We highlight system modifications to address this within the VHA. These examples may be helpful for other health care systems interested in moving toward whole health. It will be vital for health care systems integrating a whole health approach to develop both practical and educational interventions to address the communication gap. These interventions will need to target both providers and patients in health care systems that transition to a whole health model of care, not just the VHA. Both the communication gap between providers and patients, and the lack of support within some EHR systems for sexual health assessment are barriers to assessing sexual health in primary care clinics. Routine sexual health assessment would benefit patient well-being and present an opportunity to address health disparities for LGBTQ+ populations. Health care systems (ie, both the VHA and other systems) can overcome these barriers by implementing educational interventions and updating their EHRs and back-end data structures. VHA's expertise in developing and implementing health education interventions and EHR-based quality improvements may help inform interventions beyond VHA.

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)0.021
metaresearch head score (Gemma)0.076
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.076
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0070.007
Open science0.0020.008
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0300.011

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 head0.070
GPT teacher head0.463
Teacher spread0.393 · 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 designNot applicable
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

Citations31
Published2022
Admission routes1
Has abstractyes

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