Free Online Decision Tools to Support Parents Making Decisions About Their Children's Chronic Health Condition: An Environmental Scan
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Medical decisions parents make on their children's behalf can be challenging. Free online decision support tools are created to help parents faced with these decisions. OBJECTIVE: We used an environmental scan to identify free, online tools that support parents in making decisions about their children's chronic health condition. We described the tools and assessed their potential to harm, content, development process, readability, and whether their use changed decision makers' knowledge and alignment of their preferences with their final decision. DATA SOURCES AND ELIGIBILITY: Decision aid repositories, Google searches, and key informants identified decision support tools. Eligible tools were freely available online and for parents of children with chronic health conditions. APPRAISAL METHODS: Two reviewers independently assessed the tools' quality based on the International Patient Decision Aid Standards (IPDAS). Tool readability was assessed using the Flesch Reading Ease test. RESULTS: From 21 free, online decision support tools, 14 (67%) provided sufficient detail for making a specific decision (IPDAS qualifying criteria). None sufficiently met IPDAS certification criteria necessary to reduce the possibility of patient harms when using the tool. Three (14%) were fairly easy or easy to read. Of those evaluated by developers (n = 6), 2 improved knowledge and 4 improved alignment of preferences with the available options. LIMITATIONS: Google searches and key informant sources are not replicable. CONCLUSIONS: Free, online decision support tools for parents of children with chronic health conditions are of variable quality, most are difficult to read, and there is limited evidence their use achieves intended outcomes. REGISTRATION NUMBER: Registered with Open Science Framework 20 July 2021(AEST) osf.io/b94yj.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it