Daily Targeted Evidence Reports for Orthopaedic Surgeons: A Mixed Methods Study in India
Bibliographic record
Abstract
Background: There is limited research on how web-based, point-of-care, evidence-based medicine (EBM) tools, such as evidence summaries, are being implemented and used in developing countries. Objectives: To investigate accessibility, use, and impact of an online EBM knowledge dissemination portal in orthopaedic surgery. To explore whether receiving daily targeted evidence summaries results in more frequent use of an EBM tool compared with receiving general weekly reports. To identify and explain the barriers and benefits of a point-of-care resource in the Indian context. Methods: Forty-four orthopaedic surgeons in Pune, India, were provided free access to OrthoEvidence (OE), a for-profit, online EBM knowledge dissemination portal. Participants were subsequently randomized to an Intervention group receiving daily targeted evidence summaries or a Control group receiving general weekly summaries. This study employed an explanatory sequential mixed methods design that incorporated two questionnaires, OE usage data, and semi-structured interviews to gain insight into the surgeons’ usage, perceptions and impact of OE. Results: There were no observable differences in OE usage between the Intervention and Control groups. OE was deemed to be comprehensive, practical, useful, and applicable to clinical practice by the majority of surgeons. The exit survey data revealed no differences between groups’ perceptions of the OE tool. Semi-structured interviews revealed barriers to keeping up with evidence that included limited access to relevant medical literature (limited internet connection, lack of time, minimal access to medical journals) and limited incentive to keep up with it (limited decision-making powers for residents, textbook-based residency curriculum, lack of research methods knowledge, limited context-specific research). Changing trauma practices at the hospital were noted following the intervention. Recommendations: The practice of EBM and the use of point-of-care tools in India can be promoted by investing in adequate electronic infrastructure (improvements to internet access) and by integrating EBM into training programs and surgical cultures.
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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".