Developing and Implementing a Web-Based Psychotherapy Program to Address Mental Health Challenges Among Patients Receiving Oncologic and Palliative Care: Protocol for an Open-Label Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The demand for mental health care, particularly for depression and anxiety, is 3-fold greater among patients receiving oncologic and palliative care than for the general population. This population faces unique barriers, making them more susceptible to mental health challenges. Various forms of psychotherapy have been deemed effective in addressing mental health challenges in this population, including supportive psychotherapy, cognitive behavioral therapy, problem-based therapy, and mindfulness; however, their access to traditional face-to-face psychotherapy resources is limited owing to their immunocompromised status, making frequent hospital visits dangerous. Additionally, patients can face hospital fatigue from numerous appointments and investigations or may live in remote areas, which makes commutes both physically and financially challenging. Web-based psychotherapy is a promising solution to address these accessibility barriers. Moreover, web-based psychotherapy has been proven effective in addressing depression and anxiety in other populations and may be implementable among patients receiving oncologic and palliative care. OBJECTIVE: The study will investigate the feasibility and effectiveness of web-based psychotherapy among patients receiving oncologic and palliative care, who have comorbid depression or anxiety. We hypothesized that this program will be a viable and efficacious treatment modality compared to current treatment modalities in addressing depression and anxiety symptoms in this population. METHODS: Participants (n=60) with depression or anxiety will be recruited from oncology and palliative care settings in Kingston (Ontario, Canada). Participants will be randomly allocated to receive either 8 weeks of web-based psychotherapy plus treatment as usual (treatment arm) or treatment as usual exclusively (control arm). The web-based psychotherapy program will incorporate cognitive behavioral therapy, mindfulness, and problem-solving skills, and homework assignments with personalized feedback from a therapist. All web-based programs will be delivered through a secure platform specifically designed for web-based psychotherapy delivery. To evaluate treatment efficacy, all participants will complete standardized symptomology questionnaires at baseline, midpoint (week 4), and posttreatment. RESULTS: The study received ethics approval in February 2021 and began recruiting participants in April 2021. Participant recruitment has been conducted through social media advertisements, physical advertisements, and physician referrals. To date, 11 participants (treatment, n=5; control, n=4; dropout, n=2) have been recruited. Data collection and analysis are expected to conclude by December 2021 and January 2022, respectively. Linear regression (for continuous outcomes) will be conducted with interpretive qualitative methods. CONCLUSIONS: Our findings can be incorporated into clinical policy and help develop more accessible mental health treatment options for patients receiving oncologic and palliative care. Asynchronous and web-based psychotherapy delivery is a more accessible, scalable, and financially feasible treatment that could have major implications on the health care system. TRIAL REGISTRATION: ClinicalTrials.gov NCT04664270; https://clinicaltrials.gov/ct2/show/NCT04664270. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30735.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.071 | 0.012 |
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".