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The Impact of a Multimedia Informational Intervention on Healthcare Service Use Among Women and Men Newly Diagnosed With Cancer

2008· article· en· W1984205118 on OpenAlexafffundabout
Carmen G. Loiselle, Sylvie Dubois

Bibliographic record

VenueCancer Nursing · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsMcGill University
FundersUniversité de MontréalMcGill University
KeywordsMedicineIntervention (counseling)CancerFamily medicineBreast cancerHealth carePhysical therapyGerontologyNursingInternal medicine

Abstract

fetched live from OpenAlex

This quasi-experimental longitudinal study documented the impact of a comprehensive cancer informational intervention using information technology on healthcare service use among individuals newly diagnosed with cancer. Women with breast cancer (n = 205) and men with prostate cancer (n = 45) were recruited within 8 weeks of diagnosis at 4 university teaching hospitals in Montreal, Quebec, Canada. The intervention group (n = 148) received a 1-hour training on information technology use, a CD-ROM on cancer, and a list of reputable cancer-related Web sites. The intervention material was available for a period of 8 weeks. The control group (n = 102) received usual care. Self-reported questionnaires were completed at T1 (baseline), T2 (1 week after intervention), and T3 (3 months after intervention). Using multivariate statistics, the experimental group reported significantly more satisfaction with cancer information received compared to the control group. No significant differences were found between experimental and control groups in their reliance on healthcare services. However, women as opposed to men spent more time with nurses, were more satisfied with cancer information received, and relied more heavily on health services. Future research would explore whether the latter observations reflect genuine sex differences or are more contingent on the specific cancer diagnosis.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.458
Teacher spread0.397 · 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 designObservational
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

Citations21
Published2008
Admission routes3
Has abstractyes

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