Availability of researcher-led eHealth tools for pain assessment and management: barriers, facilitators, costs, and design
Bibliographic record
Abstract
INTRODUCTION: Numerous eHealth tools for pain assessment and management have been developed and evaluated with promising results regarding psychometric properties, efficacy, and effectiveness. Although considerable resources are spent on developing and evaluating these tools with the aim of increasing access to care, current evidence suggests they are not made available to end users, reducing their impact and creating potential research waste. METHODS: This study consisted of 2 components: (1) a systematic review of eHealth tools for pediatric pain assessment and/or management published in the past 10 years, and (2) an online survey, completed by the authors of identified tools, of tool availability, perceived barriers or facilitators to availability, grant funding used, and a validated measure of user-centeredness of the design process (UCD-11). RESULTS: Ninety articles (0.86% of citations screened) describing 53 tools met inclusion criteria. Twenty-six survey responses were completed (49.06%), 13 of which (50.00%) described available tools. Commonly endorsed facilitators of tool availability included researchers' beliefs in tool benefits to the target population and research community; barriers included lack of infrastructure and time. The average cost of each unavailable tool was $314,425.31 USD ($3,144,253.06 USD total, n = 10). Authors of available tools were more likely to have followed user-centered design principles and reported higher total funding. CONCLUSION: Systemic changes to academic and funding structures could better support eHealth tool availability and may reduce potential for research waste. User-centered design and implementation science methods could improve the availability of eHealth tools and should be further explored in future studies.
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 | 0.141 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".