A Qualitative Protocol for Evaluating Online Social Work Interventions for Cancer Survivors Using an AI-Enhanced WeChat Mini-Application
Bibliographic record
Abstract
Cancer survivors in China face ongoing physiological, psychological, social, and spiritual challenges that significantly impact their quality of life. Despite the proliferation of online interventions, existing research lacks comprehensive exploration of integrating theoretical knowledge and practical experiences into the design of digital interventions for cancer survivors. To address these multidimensional needs and identified research gaps, this study presents a structured protocol for developing and evaluating an AI-enhanced online social work intervention delivered via a WeChat mini-application. Employing a three-phase, evidence-based qualitative methodological framework, the research begins with a systematic review and meta-analysis to establish comprehensive guidelines for intervention design. This is followed by an iterative multi-stakeholder co-creation process involving survivors, family members, social workers, healthcare professionals, and technology developers to optimize the mini-application. The final phase involves qualitative evaluations of the intervention to assess participant satisfaction, usability, perceived effectiveness, and sustainability of engagement. Findings from this study contribute both practically—by demonstrating how AI technology and stakeholder collaboration can deliver tailored digital interventions—and methodologically, by validating a structured, adaptable framework for designing culturally sensitive and user-centered health interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".