Closing the COVID-19 Psychological Treatment Gap for Cancer Patients in Alberta: Protocol for the Implementation and Evaluation of Text4Hope-Cancer Care
Bibliographic record
Abstract
BACKGROUND: Cancer diagnoses and treatments usually engender significant anxiety and depressive symptoms in patients, close relatives, and caregivers. Providing psychological support during the coronavirus disease (COVID-19) pandemic presents additional challenges due to self-isolation and social or physical distancing measures in place to limit viral spread. This protocol describes the use of text messaging (Text4Hope-Cancer Care) as a convenient, cost-effective, and accessible population-level mental health intervention. As demonstrated in previous research, this evidence-based program supports good outcomes and high user satisfaction. OBJECTIVE: We will implement daily supportive text messaging as a way of reducing and managing anxiety and depression related to cancer diagnosis and treatment in Alberta, Canada. Prevalence of anxiety and depressive symptoms, their demographic correlates, and Text4Hope-Cancer Care-induced changes in anxiety and depression will be evaluated. METHODS: Alberta residents with a cancer diagnosis and the close relatives of those dealing with a cancer diagnosis can self-subscribe to the Text4Hope-Cancer Care program by texting "CancerCare" to a dedicated text number. Self-administered, anonymous, online questionnaires will be used to assess anxiety and depressive symptoms using the Hospital Anxiety and Depression Scale (HADS). Data will be collected at onset from individuals receiving text messages, and at the mid- and endpoints of the program (ie, at 6 and 12 weeks, respectively). Data will be analyzed with parametric and nonparametric statistics for primary outcomes (ie, anxiety and depressive symptoms) and usage metrics, including the number of subscribers and user satisfaction. In addition, data mining and machine learning analysis will focus on determining subscriber characteristics that predict high levels of symptoms of mental disorders, and may subsequently predict changes in those measures in response to the Text4Hope-Cancer Care program. RESULTS: The first research stage, which was completed in April 2020, involved the creation and review of the supportive text messages and uploading of messages into a web-based text messaging service. The second stage, involving the launch of the Text4Hope-Cancer Care program, occurred in May 2020. CONCLUSIONS: Text4Hope-Cancer Care has the potential to provide key information regarding the prevalence rates of anxiety and depressive symptoms in patients diagnosed or receiving care for cancer and their caregivers. The study will generate demographic correlates of anxiety and depression, and outcome data related to this scalable, population-level intervention. Information from this study will be valuable for health care practitioners working in cancer care and may help inform policy and decision making regarding psychological interventions for cancer care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/20240.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".