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Record W2000658491 · doi:10.1080/07347331003689128

Exploratory Survey of Patients' Needs and Perceptions of Psychosocial Oncology

2010· article· en· W2000658491 on OpenAlexaffabout
Michèle Preyde, Pat Chevalier, Jane Hatton-Bauer, Melanie Barksey

Bibliographic record

VenueJournal of Psychosocial Oncology · 2010
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsGuelph General HospitalGrand River HospitalUniversity of Guelph
Fundersnot available
KeywordsPsychosocialSocial supportDistressMedicineIntervention (counseling)Exploratory researchFamily medicinePopulationPsycho-oncologySocial workNeeds assessmentOncologyPsychologyNursingClinical psychologyPsychiatryPsychotherapistEnvironmental health

Abstract

fetched live from OpenAlex

Cancer is a major health issue that affects a significant proportion of the population. Advancements in oncology treatment have reduced mortality, creating an ever-greater need for psychosocial oncology. Patients with cancer at Grand River Regional Cancer Centre (GRRCC) have access to some psychosocial intervention (e.g., wellness workshops, social work intervention); however, the extent to which these efforts meet patients' current needs is not known. The purpose of the exploratory survey was to assess patients' psychosocial needs and psychosocial oncology service needs. Patients receiving treatment for cancer at GRRCC were asked to participate in the anonymous survey. Two research assistants from the University of Guelph obtained informed consent, then with assistance from volunteers from the GRRCC, collected all data. The two screening tools, and standardized, self-report measures of depression and social support (Perceived Social Support Scale) were administered. Patients rated the psychosocial oncology services as very helpful, though 100% indicated the presence of distress. The main source of distress concerned not knowing what their personal outcomes will be. Implications for practice and research 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)0.001
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 score0.088
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.025
GPT teacher head0.358
Teacher spread0.333 · 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
Published2010
Admission routes2
Has abstractyes

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