Physician virtual community and medical decision making
Bibliographic record
Abstract
Purpose – Healthcare (HC) utilizes informatics to provide its services through information technology (IT) where social network is a promising initiative to aid medical decision-making (DM) quality. Even though HC is a globally expensive investment and a complex service-oriented industry, it still suffers from quality due to frequently occurring medical errors. In order to reduce medical errors, medical DM needs to be improved. This research has participated in this effort by exploring social capital theory (SCT) within a virtual community of practice (VCoP), HC knowledge management (KM) process – knowledge sharing quality and medical DM quality. Design/methodology/approach – Traditional and up-to-date HC-related and non-HC-related theoretical, empirical and case study-based literature review has been thoroughly analyzed to hence support the inter-relationships between the theoretical constructs being: SCT, knowledge sharing quality and medical DM quality presented as a conceptual framework. This conceptual framework is based on propositions derived from thorough literature review theory to relate between each of the constructs. Findings – SCT has the potential to facilitate medical DM within a VCoP, as well as, knowledge sharing quality plays a mediating and facilitating role between SCT and medical DM quality. Research limitations/implications – The study has significantly focussed on SCT, which is actually part of the social sciences and anthropology discipline. In parallel, this research also analyzed the literature that pertained to the value of a VCoP to improve medical DM quality. Practical implications – The study's focus on SCT, knowledge sharing and medical DM; hence promotes future empirical research findings to be compared within a particular HC VC; from a case study point of view. Originality/value – The paper adds value to the large body of intellectual knowledge by enhancing the conception of medical DM quality to improve HC quality. Medical DM is a soft area of research. This research has introduced a new avenue on which HC quality can be improved by reducing medical errors, from the perspective of social computing and medical DM quality and the mediating role of knowledge sharing quality.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".