Barriers to participation in HIV vaccine trials and cancer trials: a cost-gain analysis
Bibliographic record
Abstract
Previous studies examining barriers and willingness to participate in HIV vaccine trials have demonstrated the role of factors identified by the Health Belief Model. Barriers to cancer trials can also be understood in terms of a theoretical framework consisting of the locus of the barrier (personal vs. social) and the nature of the barrier (risk vs. cost). In this systematic review of review articles, we extend this framework to another life-threatening disease, cancer. The purpose is to improve conceptual clarity about volunteering in clinical trials by comparing and contrasting barriers in these two areas. In 2012, two people independently searched the Cochrane Database for Systematic Reviews, Pubmed, Embase, and Google Scholar to identify review articles examining cancer trial barriers to participation. Search terms used were: “cancer”, “oncology”, “cancer trials”, “oncology trials”, “clinical trials”, “medical research”, “willingness to participate”, “barriers”. Review articles were also retrieved from our search examining motivators to participation in cancer research and from bibliographic references. We retrieved 19 review articles from 2000-2012 examining barriers to participation in cancer trials. “Reduced quality of life” / “distrust of institutions” / “loss of control” were personal risks (PR). “Perceptions of the provider” / “subjective norms” were social risks (SR). “Side effects” / “experimental nature of the trial” were personal costs (PC). Misconceptions included “confidentiality concerns”. Consistent with HIV vaccine trials, most barriers with regards to cancer trials were related to PR and PC. More misconceptions were identified in HIV vaccine preparedness studies (VPS). Personal risk, PC, and SR barriers were similar to those identified in HIV VPS, but more misconceptions were identified in the latter. Understanding barriers can result in better recommendations on how to overcome these barriers. A limitation is that cancer populations are different than those affected by HIV.
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 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".