Improving Access to Cancer Guidelines: Feedback from Health Care Professionals
Bibliographic record
Abstract
PURPOSE: We examined access to locally developed and other available clinical practice guidelines (cpgs) for the management of cancer and evaluated how to improve uptake. METHODS: A 12-question online survey was administered to 772 members of 12 multidisciplinary tumour teams in a Canadian provincial oncology program. The teams are composed of physicians, surgeons, nurses, allied health professionals, and researchers involved in the provision of cancer care across the province. Many of these individuals construct or provide input into the provincial cpgs. The questionnaires were administered online and were completed voluntarily. RESULTS: Responses were received from 232 individuals, a response rate of 30.1%. Most respondents (75.1%) indicated they actively referenced cpgs for cancer treatment. Of the 177 respondents who identified barriers to cpg access, 24.9% said that the cause was being too busy; 24.3% and 22.6% cited the user-unfriendliness of the Web site and a lack of awareness about the cpgs. When asked about innovative changes that could be made to improve access, the creation of cpg summary documents was identified as the most effective change (46.3%). The creation of summary documents was ranked highest by physicians, surgeons, and nurses. CONCLUSIONS: Clinical practice guidelines are important tools for standardizing treatment protocols and improving outcomes in health care systems, but support for their use is variable among health care professionals. We have identified barriers to-and potential mitigating strategies for-more widespread access to cpgs by the various health professions involved in cancer care. Local creation of succinct and easily accessible cpgs was identified as the single most effective way to enhance access by health care professionals.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".