An Online Learning Module to Increase Self-Efficacy and Involvement in Care for Patients With Advanced Lung Cancer: Research Protocol
Bibliographic record
Abstract
BACKGROUND: Improving patient care for individuals with lung cancer is a priority due to the increasing burden of the disease globally. One way this can be done is by improving patient self-management capabilities through increasing their self-efficacy. This can improve positive outcomes for patients with chronic conditions and increase their ability to manage the challenges of such illnesses. Unfortunately, patients with chronic conditions often struggle to travel far from home to engage with patient education events, a common means of improving self-efficacy. The development of more accessible tools for improving patient self-efficacy is required to increase quality of life for patients with chronic conditions. OBJECTIVE: To evaluate the feasibility of delivering symptom identification and management information to patients with advanced lung cancer using an online program. METHODS: This article describes a pre-post test study to evaluate a Qstream online learning platform to improve patient self-efficacy for managing advanced lung cancer symptoms. Undertaking this program should increase participant knowledge about the side-effects they may experience as a result of their treatment and in turn increase help-seeking behavior and self-efficacy for the participant cohort. Quantitative data collected by the Qstream platform on the completion rates of participants will be used as a tool to evaluate the intervention. Additionally, validated scales will be used to collect data on patient self-efficacy. Qualitative data will also be collected via an exit survey and thematic content analysis of semi-structured interviews. RESULTS: The research is in the preliminary stages but thus far a protocol has been approved in support of the project. Additionally, advisory committee members have been identified and initial meetings have been undertaken. CONCLUSIONS: Development of new approaches for increasing patient understanding of their care is important to ensure high quality care continues to be delivered in the clinical setting.
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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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.071 | 0.014 |
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".