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Record W2624225784 · doi:10.1002/pon.4471

2016 President's Plenary International Psycho-Oncology Society: challenges and opportunities for growing and developing psychosocial oncology programmes worldwide

2017· article· en· W2624225784 on OpenAlexaff
Luzia Travado, Barry D. Bultz, Andreas Ullrich, Chioma Asuzu, Jane Turner, Luigi Grassi, Paul B. Jacobsen

Bibliographic record

VenuePsycho-Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsCanadian Association of Psychosocial OncologyAlberta Cancer FoundationUniversity of Calgary
FundersWorld Health Organization
KeywordsPsychosocialInitial public offeringMedicineHealth carePolitical scienceOncologyFamily medicineBusinessPsychiatryFinance

Abstract

fetched live from OpenAlex

Consistent with the International Psycho-Oncology Society's (IPOS) vision and goals, we are committed to improving quality cancer care and cancer policies through psychosocial care globally. As part of IPOS's mission, upon entering "Official Relations" for a second term with the World Health Organization (WHO), IPOS has dedicated much attention to reaching out to countries, which lack formalized psychosocial care programmes. One of IPOS's strategies to accomplish this goal has been to bring psycho-oncology training programmes to low- and middle-income countries and regions. To this end, the IPOS Board approved a new position on the Board of Directors for a member from a low- to middle-income country (LMIC). The IPOS 2016 President's Plenary focused on challenges and opportunities that exist in growing and developing psychosocial oncology programmes worldwide. The plenary presentations highlight how IPOS and WHO have aligned their goals to help LMICs support cancer patients as an essential element of cancer and palliative care. IPOS country representatives are strongly supported in liaising with national health authorities and with WHO Country Representatives in LMICs. The plenary speakers discussed the role IPOS Federation has taken in building a global network of psychosocial leaders and the impact this had in assisting LMICs in meeting IPOS's psychosocial care objectives. The plenary highlighted the challenges of expanding psychosocial reach into these countries. One significant question remains: Can psychosocial guidelines be adapted to LMICs and regions?

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.359
GPT teacher head0.504
Teacher spread0.146 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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