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Record W1570927137 · doi:10.1002/pon.2102

Screening for distress, the sixth vital sign: examining self‐referral in people with cancer over a one‐year period

2011· article· en· W1570927137 on OpenAlexafffundabout
Amy Waller, Andrea Williams, Shannon L. Groff, Barry D. Bultz, Linda E. Carlson

Bibliographic record

VenuePsycho-Oncology · 2011
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsOccupational Cancer Research CentreUniversity of Calgary
FundersCanadian Institutes of Health ResearchFondation pour la Recherche MédicaleAlberta Cancer Foundation
KeywordsPsychosocialMedicineReferralDistressPopulationAnxietyFamily medicinePsychiatryClinical psychologyEnvironmental health

Abstract

fetched live from OpenAlex

PURPOSE: Although research has indicated a diagnosis of cancer is most often distressing for patients and their families, few studies have examined which patients access resources to manage distress or how distress levels affect resource utilization. This study explored psychosocial and supportive care resource utilization in a large cancer population at a Canadian tertiary cancer centre over a 12-month period in a usual care setting. METHODS: Patients who were new to the Tom Baker Cancer Centre completed the Distress Thermometer, the Pain and Fatigue Thermometers, the Psychological Screen for Cancer (Part C) that measures anxiety and depression, self-report questions on resources accessed and a demographic form at baseline, 3, 6 and 12 months. No feedback or specific triage to services was provided in order to observe usual care practices. RESULTS: A total of 714 patients provided baseline data with 505 retained at 12 months. Twenty-four percent indicated they accessed at least one service (e.g. individual counselling, nutritionist or resource social worker) over the 12 months. Patients who were older, less educated and with lower income were less likely to access services. People who reported higher symptom burden were more likely to access services at each time point. CONCLUSIONS: Overall levels of access of psychosocial services were relatively low in this population and varied by socio-demographic variables and symptom burden. Routine monitoring of psychosocial, practical and physical concerns is a potential strategy for targeting individuals who may require additional information or support in accessing available services to manage their concerns.

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 score0.146
Threshold uncertainty score0.973

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)0.0010.000

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 head0.074
GPT teacher head0.338
Teacher spread0.265 · 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

Citations68
Published2011
Admission routes3
Has abstractyes

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