<scp>IT</scp>‐based clinical knowledge management in primary health care: A conceptual framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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?
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.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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