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Record W2803109232 · doi:10.1080/07347332.2018.1447528

Survivorship care needs among LGBT cancer survivors

2018· article· en· W2803109232 on OpenAlexfundno aff
Julia Seay, Darryl Mitteldorf, Alena Yankie, William F. Pirl, Erin Kobetz, Matthew Schlumbrecht

Bibliographic record

VenueJournal of Psychosocial Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersGarron Family Cancer CentreHospital for Sick Children
KeywordsSurvivorship curvePsychosocialSocial supportNeeds assessmentMedicineGerontologyHuman sexualityLesbianPsychologyFamily medicineCancerPsychiatryInternal medicinePsychotherapist

Abstract

fetched live from OpenAlex

OBJECTIVE: To better understand survivorship care needs among LGBT cancer survivors. DESIGN: We administered an anonymous online survey. SAMPLE: LGBT cancer survivors living in the United States. METHODS: Participants were recruited via the National LGBT Cancer Project. The survey measured sociodemographic characteristics, social support, posttraumatic stress, and survivorship care needs. RESULTS: Approximately 72% of our 114 participants were cisgender male and 87% were white. Almost all participants reported at least some unmet survivorship care needs (73%), with over half of participants reporting unmet psychological and sexuality care needs. Participants who reported their oncologist was not LGBT-competent had greater unmet needs (t(82) = 2.5, p = 0.01) and greater posttraumatic stress (t(91) = 2.1, p = 0.035). CONCLUSIONS: LGBT cancer survivors have significant unmet survivorship care needs, and lack of oncologist LGBT-competence is associated with unmet needs. Implications for Psychosocial Providers: Our results suggest the need for LGBT competency training for providers.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.362
Teacher spread0.338 · 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 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

Citations30
Published2018
Admission routes1
Has abstractyes

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