MétaCan
Menu
Back to cohort
Record W4310588841 · doi:10.1002/pon.6078

Global psycho‐oncology in low middle‐income countries: Challenges and opportunities

2022· article· en· W4310588841 on OpenAlexaff
Jeff Dunn, Gary Rodin

Bibliographic record

VenuePsycho-Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsPsychosocialPsychological interventionLow and middle income countriesGlobal healthMedicinePsycho-oncologyDeveloping countryNursingPsychologyEconomic growthPsychiatryPublic health

Abstract

fetched live from OpenAlex

OBJECTIVES: This Special Issue of Psycho-Oncology is focused on challenges and opportunities in the provision of psychosocial care to patients in low and middle-income countries (LMICs). The aim is to highlight global disparities and inequity in the provision of evidence-based, culturally-sensitive and timely psychosocial care and to showcase the work of researchers and practitioners to address this gap. We hope that this Issue will help to advance the psychological and social dimensions of cancer care in all parts of the world. METHODS: The focus of the papers is on research and clinical innovations in LMICs that target the psychological, social and cultural dimensions of cancer and on interventions to improve or maintain the psychological well-being, social functioning and/or quality of life of those who are affected and their families. RESULTS: These papers draw attention to guidelines, resource needs, clinical service evaluation, emerging research and knowledge translation within LMICs that advance knowledge and implementation in the field of psycho-oncology. CONCLUSIONS: Innovations and advances in psycho-oncology are emerging from LMICs to enhance the care of patients with cancer and their families in these regions and in all parts of the world. A sustained global initiative is now needed to ensure that guidelines for such care are routinely included in global, national and local cancer control plans and that essential resources and attention are directed to implement them.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score1.000

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)0.0020.000

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 head0.077
GPT teacher head0.351
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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuePsycho-OncologySame topicCancer survivorship and careFrench-language works237,207