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Record W2084407534 · doi:10.1037/0278-6133.24.5.488

A Systematic Review of Studies Evaluating Diffusion and Dissemination of Selected Cancer Control Interventions.

2005· review· en· W2084407534 on OpenAlexaff
Peter Ellis, Paula D. Robinson, Donna Ciliska, Tanya Armour, Melissa Brouwers, Mary Ann O’Brien, Jonathan Sussman, Parminder Raina

Bibliographic record

VenueHealth Psychology · 2005
Typereview
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsMcMaster UniversityCancer Care OntarioHamilton Regional Laboratory Medicine Program
Fundersnot available
KeywordsPsychological interventionMedicineDisseminationInformation DisseminationSmoking cessationCancerBehavior changeCervical cancerSystematic reviewIntervention (counseling)MEDLINEFamily medicineNursingPathologyInternal medicine

Abstract

fetched live from OpenAlex

With this review, the authors sought to determine what strategies have been evaluated (including the outcomes assessed) to disseminate cancer control interventions that promote the uptake of behavior change. Five topic areas along the cancer care continuum (smoking cessation, healthy diet, mammography, cervical cancer screening, and control of cancer pain) were selected to be representative. A systematic review was conducted of primary studies evaluating dissemination of a cancer control intervention. Thirty-one studies were identified that evaluated dissemination strategies in the 5 topic areas. No strong evidence currently exists to recommend any one dissemination strategy as effective in promoting the uptake of cancer control interventions. The authors conclude that there is a strong need for more research into dissemination of cancer control interventions. Future research should consider methodological issues such as the most appropriate study design and outcomes to be evaluated.

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)0.025
metaresearch head score (Gemma)0.122
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.122
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0100.007
Bibliometrics0.0190.021
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.367
GPT teacher head0.656
Teacher spread0.289 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations80
Published2005
Admission routes1
Has abstractyes

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