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Record W4393095990 · doi:10.1007/s11764-024-01565-8

Figure it out on your own: a mixed-method study on pelvic health survivorship care after gynecologic cancer treatments

2024· article· en· W4393095990 on OpenAlexaff
Stéphanie Bernard, Ericka Wiebe, Alexandra Waters, Sabrina Selmani, Jill Turner, Sinéad Dufour, Puneeta Tandon, Donna Pepin, Margaret L. McNeely

Bibliographic record

VenueJournal of Cancer Survivorship · 2024
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsOvarian Cancer CanadaMcMaster UniversityAlberta Health ServicesCentre Intégré de Santé et de Services Sociaux du Bas-Saint-LaurentUniversité LavalUniversity of Alberta
Fundersnot available
KeywordsMedicinePsychological interventionEmbarrassmentHealth careThematic analysisFocus groupSurvivorship curveNursingReproductive healthFamily medicinePhysical therapyQualitative researchPsychologyPopulationPsychotherapistEnvironmental health

Abstract

fetched live from OpenAlex

PURPOSE: Pelvic health issues after treatment for gynecological cancer are common. Due to challenges in accessing physiotherapy services, exploring virtual pelvic healthcare is essential. This study aims to understand needs, preferences, barriers, and facilitators for a virtual pelvic healthcare program for gynecological cancer survivors. METHODS: A multi-center, sequential mixed-methods study was conducted. An anonymous online survey (N=50) gathered quantitative data on pelvic health knowledge, opportunities, and motivation. Focus groups (N=14) explored patient experiences and consensus on pelvic health interventions and virtual delivery. Quantitative data used descriptive statistics, and focus group analyses employed inductive thematic analysis. Findings were mapped to the capability, opportunity, and motivation (COM-B) behavior change model. RESULTS: Participants reported lacking knowledge about pelvic health interventions and capability related to the use of vaginal dilators and continence care. Barriers to opportunity included lack of healthcare provider-initiated pelvic health discussions, limited time in clinic with healthcare providers, finding reliable information, and cost of physical therapy pelvic health services. Virtual delivery was seen favorably and may help to address motivational barriers related to embarrassment and frustration with care. CONCLUSION: Awareness of pelvic healthcare is lacking among people treated for gynecological cancer. Virtual delivery of pelvic health interventions is perceived as a solution to enhance access while minimizing travel, cost, embarrassment, and exposure risks. IMPLICATIONS FOR CANCER SURVIVORS: A better understanding of the pelvic health needs of individuals following gynecological cancer treatments enables the development of tailored virtual pelvic health rehabilitation interventions which may improve access to pelvic health survivorship 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 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)0.014
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.058
GPT teacher head0.417
Teacher spread0.359 · 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 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
Published2024
Admission routes1
Has abstractyes

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