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Examining patient and visit characteristics associated with the cancer patient experience.

2019· article· en· W2980834768 on OpenAlexafffundabout
Erica Bridge, Simron Singh, Amanda Murdoch, Mindaugas Mozuraitis, Brett Nicholls, Lesley Moody

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreCancer Care Ontario
FundersCancer Care Ontario
KeywordsMedicineLogistic regressionCancerQuartileLung cancerInternal medicineDemographyConfidence interval

Abstract

fetched live from OpenAlex

204 Background: Your Voice Matters (YVM) is a 28-item patient experience survey for adult cancer outpatients undergoing treatment in Ontario.The purpose was to examine patient and visit characteristics associated with YVM responses. Methods: YVM was administered to eligible patients (n=8,704) at 14 centres in 2017.Respondents answered items on a five-point Likert scale (4 or 5 = positive response). A multivariable logistic regression (MLR) was undertaken for each YVM item.Variables (reference group) included: disease site (heamatology), age group (65+), sex (female), rurality (urban), last visit type (consult), income quintile(highest) and immigration tercile (lowest). Significant results ( p<.05) vary by MLR model. Therefore a p-value range is provided for all significant variables for all MLR models. Results: Patients were more likely to have a less positive experience if they had central nervous system ( p =.000-.013), gastrointestinal ( p =.004-.026), head and neck ( p =.002-.025), or lung cancer ( p =.017-.049); were between the ages of 18-39 years ( p = <.0001-.048); were female ( p =.0001-.022); received chemotherapy( p =.0001-.047); lived in a rural location ( p =.042); from a mid-( p =.027-.045) and mid-high income quartile ( p =.021); and in the high- ( p =.002-.043) and middle-immigrant terciles ( p =.028-.047), when compared to the reference group. Patient were more likely to have a more positive experience if they had skin cancer ( p =.004-.032); were between the ages of 40-64 years ( p =.003-.033); receiving radiation ( p =.0001-.041) or a minor procedure ( p =.015); and were from the mid-low ( p =.003-.042) and low income quartile ( p =.001-.032), when compared to the reference group. Conclusions: The cancer patient experience varies by patient and visit characteristics. Future initiatives should examine these characteristics to better understand their populations to create a more tailored approaches to cancer 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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.273
GPT teacher head0.547
Teacher spread0.275 · 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 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".

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Citations5
Published2019
Admission routes3
Has abstractyes

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