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Record W3177029026 · doi:10.1097/or9.0000000000000051

From foundation to inspiration: implementing screening for distress (6th Vital Sign) for optimal cancer care—international leadership perspectives on program development

2021· article· en· W3177029026 on OpenAlexaff
Barry D. Bultz, Linda Watson, Matthew Loscalzo, Brian Kelly, James Zabora

Bibliographic record

VenueJournal of Psychosocial Oncology Research and Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsPsychosocialDistressFoundation (evidence)Context (archaeology)Presentation (obstetrics)MedicineNursingPsychologyPsychotherapistPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Abstract The principles of whole patient care in cancer and the evidence regarding the benefits of screening for distress provide the impetus for innovation in implementation of psychosocial oncology programs. This includes the creation of new ways of integrating psychosocial assessment in patient reported outcomes and linking this to models of interdisciplinary collaborative care. Screening for distress can itself promote engagement of patients and families/carers in their care. To achieve this, recognition of the broader interpersonal and social context of cancer and related concerns for patients in such screening practices is encouraged. This article will lay the foundation for the successful implementation of clinical distress screening programs and then outline strategies that have been demonstrated to be successful in program quality, growth, and resource preservation. A brief overview of historical foundations of screening for distress is provided along with presentation of examples of innovative practice, including evidence of broader benefits of such screening and future challenges to effective program development, along with including recommendations for implementation within cancer care services.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.287
GPT teacher head0.562
Teacher spread0.274 · 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 designOther design
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
Published2021
Admission routes1
Has abstractyes

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