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Record W2075903810 · doi:10.1002/pon.767

Development and pilot testing of a psychoeducational intervention for oral cancer patients

2003· article· en· W2075903810 on OpenAlexaff
Mark R. Katz, Jonathan C. Irish, Gerald M. Devins

Bibliographic record

VenuePsycho-Oncology · 2003
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsPrincess Margaret Cancer CentreToronto General HospitalUniversity of TorontoUniversity Health Network
FundersNational Cancer Institute
KeywordsPsychoeducationPsychosocialMedicineRandomized controlled trialDistressCoping (psychology)Intervention (counseling)AnxietyCancerPhysical therapyClinical psychologyPsychiatrySurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Oral cancer elicits considerable distress in both the pre and post-treatment periods. This paper details the development, validation and pilot-testing of a psychoeducational intervention for oral cancer patients. METHOD: An educational booklet covering information about oral cancer and its treatment and effective coping strategies was developed by a multi-disciplinary team. It was evaluated by patient focus groups and content/educational experts, delivered to subjects in pre and post-operative presentations by a health educator, and pilot-tested in a randomized controlled trial versus standard care. RESULTS: Pilot data from 19 subjects (10 psychoeducation, nine standard care) indicates that the intervention is feasible and highly acceptable. At follow-up the intervention group showed a gain in knowledge, less body image disturbance, lower anxiety and a trend toward higher wellbeing. CONCLUSION: This program, which is currently being evaluated in a larger RCT with extended follow-up, should prove useful in reducing the psychosocial burden of oral cancer and its treatment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.091
GPT teacher head0.406
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations73
Published2003
Admission routes1
Has abstractyes

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