159: Improving Patient and Family Education in Pediatric Hematology Consultations Through the Use of Printed Educational Materials
Bibliographic record
Abstract
There is an ever increasing demand for readily available information for patients and their families in the health care system. Access to information should be provided to patients and families by all physicians. There is not currently adequate patient centered information available to all patients nor a validated mechanism for physicians to use to provide their patients with information regarding their diagnosis. To validate a method for developing new patient centered informational handouts designed to improve patient understanding of the disease process and management. A chart review of one physician was done to identify the most common diagnoses in pediatric hematology from December 2011 to May 2012. A literature review view was then conducted to identify diagnoses that already had adequate, patient centered information available. New patient centered informational handouts were generated for each of the most common diagnoses remaining. Existing literature regarding content and structure of informational handouts was identified and incorporated into the new handouts. The handouts were then reviewed and revised by a content expert. The revised handouts were reviewed by 10 patient families using both qualitative and quantitative measurements. These results were used to further revise the handouts. The 10 most common pediatric hematology diseases seen in clinic were identified. The patient families identified four major themes when asked to review the handouts. They found the information useful, although they stressed the importance of giving this information to new patients at the time of diagnosis. They stressed the importance of physician counseling, and not using the handouts to replace counseling. Lastly, the patients liked the handouts because they could take the handouts home and use them as a reference. The handouts scored highly in regards to accuracy and being recommended to other patients and their families. Using the described framework, 10 different patient centered informational handouts have been developed and validated. This strategy outlines a way for physicians and other health care workers to create their own patient centered informational handouts to best fit their needs.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".