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Record W4295678372 · doi:10.2196/39725

Role for a Web-Based Intervention to Alleviate Distress in People With Newly Diagnosed Testicular Cancer: Mixed Methods Study

2022· article· en· W4295678372 on OpenAlexvenueno aff
Ciara Conduit, Christina Guo, Allan Ben Smith, Orlando Rincones, Olivia Baenziger, Benjamin Thomas, Jeremy Goad, Dan Lenaghan, Nathan Lawrentschuk, Lih‐Ming Wong, Niall M. Corcoran, Margaret Ross, Peter Gibbs, Sophie O’Haire, A. Antón, Elizabeth Liow, Jeremy Lewin, Ben Tran

Bibliographic record

VenueJMIR Cancer · 2022
Typearticle
Languageen
FieldMedicine
TopicTesticular diseases and treatments
Canadian institutionsnot available
FundersMovember Foundation
KeywordsDistressAnxietyMedicineDepression (economics)Coping (psychology)Clinical psychologyPsychiatryPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Distress is common immediately after diagnosis of testicular cancer. It has historically been difficult to engage people in care models to alleviate distress because of complex factors, including differential coping strategies and influences of social gender norms. Existing support specifically focuses on long-term survivors of testicular cancer, leaving an unmet need for age-appropriate and sex-sensitized support for individuals with distress shortly after diagnosis. OBJECTIVE: We evaluated a web-based intervention, Nuts & Bolts, designed to provide support and alleviate distress after diagnosis of testicular cancer. METHODS: Using a mixed methods design to evaluate the acceptability, feasibility, and impact of Nuts & Bolts on distress, we randomly assigned participants with recently diagnosed testicular cancer (1:1) access to Nuts & Bolts at the time of consent (early) or alternatively, 1 week later (day 8; delayed). Participants completed serial questionnaires across a 4- to 5-week period to evaluate levels of distress (measured by the National Comprehensive Cancer Network Distress Thermometer [DT]; scored 0-10), anxiety, and depression (Hospital Anxiety and Depression Score [HADS]-Anxiety and HADS-Depression; each scored 0-21). The primary end point was change in distress between consent and day 8. Secondary end points of distress, anxiety, and depression were assessed at defined intervals during follow-up. Optional, semistructured interviews occurring after completion of quantitative assessments were thematically analyzed. RESULTS: Overall, 39 participants were enrolled in this study. The median time from orchidectomy to study consent was 14.8 (range 3-62) days. Moderate or high levels of distress evaluated using DT were reported in 58% (23/39) of participants at consent and reduced to 13% (5/38) after 1 week of observation. Early intervention with Nuts & Bolts did not significantly decrease the mean DT score by day 8 compared with delayed intervention (early: 4.56-2.74 vs delayed: 4.47-2.74; P=.85), who did not yet have access to the website. A higher baseline DT score was significantly predictive of reduction in DT score during this period (P<.001). Median DT, HADS-Anxiety, and HADS-Depression scores reduced between orchidectomy and 3 weeks postoperatively and then remained stable throughout the observation period. Thematic analysis of 16 semistructured interviews revealed 4 key themes, "Nuts & Bolts is a helpful tool," "Maximizing benefits of the website," "Whirlwind of diagnosis and readiness for treatment," and "Primary stressors and worries," as well as multiple subthemes. CONCLUSIONS: Distress is common following the diagnosis of testicular cancer; however, it decreases over time. Nuts & Bolts was considered useful, acceptable, and relevant by individuals diagnosed with testicular cancer, with strong support for the intervention rendered by thematic analyses of semistructured interviews. The best time to introduce support, such as Nuts & Bolts, is yet to be determined; however, it may be most beneficial as soon as testicular cancer is strongly suspected or diagnosed.

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 score0.115
Threshold uncertainty score0.901

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 head0.015
GPT teacher head0.377
Teacher spread0.362 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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