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Record W2799698792 · doi:10.2196/cancer.8918

A Web-Based Intervention to Reduce Distress After Prostate Cancer Treatment: Development and Feasibility of the Getting Down to Coping Program in Two Different Clinical Settings

2018· article· en· W2799698792 on OpenAlexvenueno aff
Jane Cockle‐Hearne, Deborah Barnett, James Hicks, Mhairi Simpson, Isabel White, Sara Faithfull

Bibliographic record

VenueJMIR Cancer · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersNational Institute for Health and Care ResearchNHS Health Scotland
KeywordsDistressCoping (psychology)Prostate cancerPeer supportIntervention (counseling)Emotional distressWeb applicationPsychologyCognitionMedicinePsychotherapistClinical psychologyAnxietyNursingCancerComputer sciencePsychiatryWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Distress after prostate cancer treatment is a substantial burden for up to one-third of men diagnosed. Physical and emotional symptoms and health service use can intensify, yet men are reticent to accept support. To provide accessible support that can be cost effectively integrated into care pathways, we developed a unique, Web-based, self-guided, cognitive-behavior program incorporating filmed and interactive peer support. OBJECTIVE: To assess feasibility of the intervention among men experiencing distress after prostate cancer treatment. Demand, acceptability, change in distress and self-efficacy, and challenges for implementation in clinical practice were measured. METHODS: A pre-post, within-participant comparison, mixed-methods research design was followed. Phase I and II were conducted in primary care psychological service and secondary care cancer service, respectively. Men received clinician-generated postal invitations: phase I, 432 men diagnosed <5 years; phase II, 606 men diagnosed <3.5 years. Consent was Web-based. Men with mild and moderate distress were enrolled. Web-based assessment included demographic, disease, treatment characteristics; distress (General Health Questionnaire-28); depression (Patient Health Questionnaire-9); anxiety (General Anxiety Disorder Scale-7); self-efficacy (Self-Efficacy for Symptom Control Inventory); satisfaction (author-generated, Likert-type questionnaire). Uptake and adherence were assessed with reference to the persuasive systems design model. Telephone interviews explored participant experience (phase II, n=10); interviews with health care professionals (n=3) explored implementation issues. RESULTS: A total of 135 men consented (phase I, 61/432, 14.1%; phase II, 74/606, 12.2%); from 96 eligible men screened for distress, 32% (30/96) entered the intervention (phase I, n=10; phase II, n=20). Twenty-four completed the Web-based program and assessments (phase I, n=8; phase II, n=16). Adherence for phase I and II was module completion rate 63% (mean 2.5, SD 1.9) versus 92% (mean 3.7, SD 1.0); rate of completing cognitive behavior therapy exercises 77% (mean 16.1, SD 6.2) versus 88% (mean 18.6, SD 3.9). Chat room activity occurred among 63% (5/8) and 75% (12/16) of men, respectively. In phase I, 75% (6/8) of men viewed all the films; in phase II, the total number of unique views weekly was 16, 11, 11, and 10, respectively. The phase II mood diary was completed by 100% (16/16) of men. Satisfaction was high for the program and films. Limited efficacy testing indicated improvement in distress baseline to post intervention: phase I, P=.03, r=-.55; phase II, P=.001, r=-.59. Self-efficacy improved for coping P=.02, r=-.41. Service assessment confirmed ease of assimilation into clinical practice and clarified health care practitioner roles. CONCLUSIONS: The Web-based program is acceptable and innovative in clinical practice. It was endorsed by patients and has potential to positively impact the experience of men with distress after prostate cancer treatment. It can potentially be delivered in a stepped model of psychological support in primary or secondary care. Feasibility evidence is compelling, supporting further evaluative research to determine clinical and cost effectiveness.

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)0.000
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 score0.329
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.040
GPT teacher head0.423
Teacher spread0.383 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

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