<scp>IT</scp>‐based clinical knowledge management in primary health care: A conceptual framework
Bibliographic record
Abstract
Primary health care medical practices have made sizable information technology investments in recent years, primarily deploying electronic medical record (EMR) systems as well as Web‐based e‐learning applications. The basic assumption here is that developing information technology‐based knowledge management capabilities may significantly improve the innovation and clinical performance of these organizations. Increasing uncertainty in their environment requires them to develop greater absorptive capacity (ACAP), that is, an organizational learning capability to deal with the external sources of this uncertainty. In applying ACAP theory to primary care settings, this study seeks to answer the following research questions: What are the e‐learning and EMR capabilities developed by primary care medical practices in response to increasing environmental uncertainty? To what extent does the development of an e‐learning capability influence the development of an EMR capability? To what extent does building ACAP contribute to positive outcomes in terms of medical practices' innovation and clinical performance?
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".