Improving Understandability of Clinical Practice Guideline Recommendations: a Qualitative Study to Develop a Format for Pediatric Cancer Care
Bibliographic record
Abstract
Abstract Background: Features of clinical practice guideline (CPG) recommendations may affect understanding and consequently successful uptake and implementation. We aimed to develop a recommendation format to improve understandability among healthcare professionals involved in pediatric cancer care.Methods: We conducted a multi-center qualitative study of health care professionals at participating pediatric oncology sites. We developed an initial format based on the current literature and used the “think-aloud” technique in multiple rounds of one-on-one cognitive interviews to iteratively improve it. Interviews were conducted until the format was well understood and no new, substantive suggestions for revision were raised. We took a directed (deductive) approach to content analysis of the interview notes to identify concerns related to the understandability of the recommendation. Results: Five investigators interviewed 33 healthcare professionals from multiple disciplines in seven rounds. We identified important factors influencing how to communicate recommendations. Regarding the strength of the recommendation, participants found understanding weak recommendations more challenging than strong. Understanding was improved by using the word ‘conditional’ instead of ‘weak’. Participants believed the inclusion of a rationale section to be very helpful. More information was desirable when a recommendation entailed a practice change. Although participants wanted additional information, they were concerned that there could be too much information. They, therefore, suggested that key words and the studies included in the evidence synthesis be hyperlinked to explanatory data. In the final format, the recommendation strength is clearly indicated in the title, highlighted, and defined within a text box. The rationale for the recommendation is in a column on the left, with supporting evidence on the right. In a bulleted list, the rationale section describes the benefits and harms and additional factors, such as implementation, that were balanced by the CPG developers. Each bullet under the supporting evidence section indicates the level of evidence with an explanation and the supporting studies with hyperlinks when applicable. Conclusions: A well-understood recommendation format to present strong and conditional recommendations was created through an iterative interview process. The format is straightforward, making it easy for organizations and CPG developers to use it to communicate recommendations clearly to intended users.
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 | 0.097 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".