Cancer Care Supportive Text Messaging Program (Text4Hope) for People Living With Cancer and Their Caregivers During the COVID-19 Pandemic: Longitudinal Observational Study
Bibliographic record
Abstract
BACKGROUND: Cancer is the leading cause of death in Canada, and living with cancer generates psychological demands, including depression and anxiety among cancer survivors and caregivers. Text4Hope-Cancer Care SMS text messaging-based service was provided to people with cancer and caregivers during the COVID-19 pandemic to support their mental health. OBJECTIVE: The aim of this study is to examine the clinical effectiveness of and satisfaction with Text4Hope-Cancer Care in addressing mental health conditions among people living with cancer and caregivers. METHODS: The study was conducted in Alberta, Canada. People who were diagnosed or receiving cancer treatment and caregivers self-subscribed to receive 3-months daily supportive cognitive behavioral therapy-based SMS text messages and a web-based survey was sent at designated time points to collect clinical and nonclinical data. The Hospital Anxiety and Depression scale (HADS) was used to examine changes in anxiety and depression symptoms after receiving the service. Satisfaction with the service was assessed using a survey with a Likert scale. Descriptive and inferential statistics were used, and test significance was considered with P≤.05. RESULTS: =2.62; P=.02), with medium effect size (Hedges g=0.7), but not depression symptoms (HADS-Depression [HADS-D] subscale). Subscribers expressed high satisfaction and agreed that the service has helped them to cope with mental health symptoms and improve their quality of life. Most subscribers read the SMS text messages more than once (30/30, 100%); took time to reflect or took a beneficial action after reading the messages (27/30, 90%); and highly agreed (27/30, >80%) with the value of the received supportive SMS text messages as being relevant, succinct, affirmative, and positive. All subscribers recommended SMS text messaging for stress, anxiety, and depression and for cancer care support (30/30, 100%). CONCLUSIONS: Text4Hope-Cancer Care was well-perceived and effectively addressed anxiety symptoms among people living with cancer and caregivers during the peak of the COVID-19 pandemic. This study provides evidence-based support and insight for policy and stakeholders to implement similar convenient, economic, and accessible mental health services that support vulnerable populations during crises. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/20240.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".