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Record W2943725945 · doi:10.5737/23688076292110115

Perceived levels of collaboration between cancer patients and their providers during radiation therapy

2019· article· en· W2943725945 on OpenAlexaffvenueabout
Charlotte Lee, Jason Wong

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsPrincess Margaret Cancer CentreToronto Metropolitan University
Fundersnot available
KeywordsRadiation TherapistMedicineDistressFamily medicineHealth professionalsHealth careNursingRadiation therapyRadiation oncologyCancerClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

This study described the patterns within collaborative relationships between patients and health care professionals during radiation therapy (RT). A one-time survey was administered to cancer patients (N=130) receiving RT at one Ontario cancer centre. The key study variables were collaboration between patients and health care providers and participants' well-being. Participants reported higher levels of collaboration with nurses, radiation oncologists, and radiation therapists than with dietitians, social workers and spiritual support personnel [F(5, 760) = 430.42, p < .001]. Participants with more symptom distress collaborated more with some health care professionals than those with less distress, but this was only true for collaboration with social workers (p < .05) and dietitians (p < .05). We postulated that participants did not require services from dietitians and social workers when symptom burden was low. Future directions regarding integration of patient-centred measures (e.g., self-management education) into interprofessional models for cancer care are discussed.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.343
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.302
Teacher spread0.283 · 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

Citations6
Published2019
Admission routes3
Has abstractyes

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