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Record W2104307569 · doi:10.1002/pon.3916

The missing piece: cancer prevention within psycho‐oncology — a commentary

2015· article· en· W2104307569 on OpenAlexaff
Zeev Rosberger, Samara Perez, Joan R. Bloom, Gilla K. Shapiro, Richard Fielding

Bibliographic record

VenuePsycho-Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsPsychosocialClinical OncologyPsycho-oncologyPsychological interventionConceptualizationMedicineGynecologic oncologySurvivorship curveOncologyCancer preventionPopulationCancerQuality of life (healthcare)Internal medicinePsychologyPsychiatryNursing

Abstract

fetched live from OpenAlex

In this commentary, we review the place of prevention within the field of Psycho-Oncology. The thrust of Psycho-Oncology's clinical and research efforts have historically focused on behavioral and social factors implicated in the cancer patients' experience from detection and diagnosis, to treatment, survivorship and end of life along the cancer trajectory. This conceptualization has raised the standards for research, leading to a better understanding of the patient experience and the delivery of highly effective interventions to improve quality of life. Emerging data on the role of potential prevention behaviors (e.g., diet and exercise, smoking cessation, screening, etc.) suggests that Psycho-Oncology has a significant role to play in understanding and intervening on a population level to reduce cancer incidence. We present and describe an expanded model of research in Psycho-Oncology which incorporates psychosocial variables in prevention research to complement Holland et al.'s (1998, 2010) original model. The implications of this model are discussed in relation to research, clinical work and training within the discipline of Psycho-Oncology. Copyright © 2015 John Wiley & Sons, Ltd.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.075
GPT teacher head0.431
Teacher spread0.356 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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