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Record W2133714473 · doi:10.1093/hsw/32.3.189

Cancer Patients' Use of Social Work Services in Canada: Prevalence, Profile, and Predictors of Use

2007· article· en· W2133714473 on OpenAlexaffabout
Tahany M. Gadalla

Bibliographic record

VenueHealth & Social Work · 2007
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocial workMental healthGerontologyAffect (linguistics)Social supportCancerMedicineDepression (economics)PsychologyEnvironmental healthPsychiatrySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

This study examines the demographic and physical and mental health characteristics of social work clients among cancer patients in Canada as compared with nonusers of social work services, and factors that affect use of social work services among cancer patients. On the basis of data from two cycles of the Canadian Community Health Survey, the study's samples include 2,703 and 2,821 Canadians living with cancer in 2000-01 and 2003, respectively. The number of Canadians with cancer who consulted social workers about their physical, emotional, or mental health increased from 31,005 to 36,427 over the study period. Results indicate that cancer patients who used social work services were in need of social support or were members of vulnerable populations. Patient's age, living arrangement, income, depression status, and physical limitations were significant predictors of service use. Findings of this study, never reported before, offer information important for identifying barriers to service use and for future planning of social work services and resources.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.058
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.019
GPT teacher head0.288
Teacher spread0.268 · 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".

Quick stats

Citations14
Published2007
Admission routes2
Has abstractyes

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