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Record W4403300660 · doi:10.1097/or9.0000000000000144

Aligning the World Health Organization's (WHO) package of interventions for rehabilitation for cancer with the mission of the International Psycho-Oncology Society's: promoting psychosocial care for all people affected by cancer

2024· article· en· W4403300660 on OpenAlexaff
Christina Signorelli, Nicolas H. Hart, Louise Mullen, Larissa Nekhlyudov, Luzia Travado, Wwt Lam, Mélissa Henry, Csaba László Dégi, Darren Haywood, Michael Jefford

Bibliographic record

VenueJournal of Psychosocial Oncology Research and Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsPsychosocialRehabilitationPsychological interventionPsycho-oncologyMedicineGerontologyHealth careNursingPsychologyPsychiatryPolitical sciencePhysical therapy

Abstract

fetched live from OpenAlex

Abstract The number of people living with and beyond cancer continues to increase worldwide, bringing significant attention to their rehabilitation needs. Globally, psychosocial services are largely inadequate, with many cancer survivors experiencing unmet psychosocial needs. The World Health Organization (WHO) recognizes rehabilitation as an essential component of universal health coverage to prevent disease-related conditions, while also improving physical and mental functioning and overall well-being. The Package of Interventions for Rehabilitation (PIR) was developed by the WHO to address the global need for rehabilitation across 20 conditions with high prevalence and high levels of associated disability, including cancer. Many aspects of the WHO PIR align with the mission and focus of the International Psycho-Oncology Society (IPOS). This commentary describes the WHO PIR for Cancer and proposes opportunities to advance cancer rehabilitation research, policy, and practice as they align with recent and ongoing initiatives of IPOS.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.068
GPT teacher head0.519
Teacher spread0.451 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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