Impact of an Online Medical Internet Site on Knowledge and Practice of Health Care Providers: A Mixed Methods Study of the Spinal Cord Injury Rehabilitation Evidence Project
Bibliographic record
Abstract
BACKGROUND: It is not known whether ongoing access to a broad-based Internet knowledge resource can influence the practice of health care providers. We undertook a study to evaluate the impact of a Web-based knowledge resource on increasing access to evidence and facilitating best practice of health care providers. OBJECTIVE: The objective of this study was to evaluate (1) the impact of the Spinal Cord Injury Rehabilitation Evidence (SCIRE) project on access to information for health care providers and researchers and (2) how SCIRE influenced health care providers' management of clients. METHODS: A 4-part mixed methods evaluation was undertaken: (1) monitoring website traffic and utilization using Google Analytics, (2) online survey of users who accessed the SCIRE website, (3) online survey of targeted end-users, that is, rehabilitation health care providers known to work with spinal cord injury (SCI) clients, as well as researchers, and (4) focus groups with health care providers who had previously accessed SCIRE. RESULTS: The online format allowed the content for a relatively specialized field to have far reach (eg, 26 countries and over 6500 users per month). The website survey and targeted end-user survey confirmed that health care providers, as well as researchers perceived that the website increased their access to SCI evidence. Access to SCIRE not only improved knowledge of SCI evidence but helped inform changes to the health providers' clinical practice and improved their confidence in treating SCI clients. The SCIRE information directly influenced the health providers' clinical decision making, in terms of choice of intervention, equipment needs, or assessment tool. CONCLUSIONS: A Web-based knowledge resource may be a relatively inexpensive method to increase access to evidence-based information, increase knowledge of the evidence, inform changes to the health providers' practice, and influence their clinical decision making.
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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 | 0.046 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".