Use of multimedia in patient and caregiver education for cancer pain management: a literature review
Bibliographic record
Abstract
Pain is one of the most prominent symptoms faced by cancer patients. It is known that patient and caregiver-targeted educational interventions addressing the proper use of pain management may provide significant clinical value. This review examines the literature surrounding the use of multimedia interventions for patient and caregiver education (PCE) on pain management compared to traditional educational interventions. A literature search was conducted in Ovid MEDLINE (1946-July Week 2, 2016), Ovid Embase (1947-2016 Week 29), and Ovid Cochrane Central Register of Controlled Trials (up to June 2016). Paired reviewers conducted title and abstract screening and full-text screening to identify experimental, quasi-experimental and cohort studies evaluating one or more multimedia-based PCE interventions focused on cancer pain and pain management and targeting patients and/or caregivers. Findings were extracted by paired reviewers and synthesized qualitatively. Of the 68 full-text papers assessed, 7 were deemed relevant, of which 5 were RCTs and 2 were observational studies. We found limited but convincing quantitative data to suggest that the use of multimedia use in pain management education for patients/caregivers has greater value-added benefit compared to standard education. While there is evidence suggesting a positive effect on pain-related outcomes with the use of multimedia-based patient and caregiver-targeted interventions, it is limited to a small number of lower-quality studies. More robust and large-scale studies are needed to supplement existing evidence and provide more insight regarding the usability and user-friendliness of these tools in practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".