Overcoming counter-knowledge through telemedicine communication technologies
Bibliographic record
Abstract
Purpose Knowledge acquired from sources of unverified information such as gossip, partial truths or lies, in this paper it is termed as "counter-knowledge." The purpose of this paper is to explore this topic through an exploration of the links between a Hospital-in-the-Home Units (HHUs) learning process (LP), counter-knowledge, and the utilization of communication technologies. The following two questions are addressed: Does the reduction of counter-knowledge result in the utilization of communication technologies? Does the development of counter-knowledge hinder the LP? Design/methodology/approach This paper examines the relevance of communication technologies to the exploration and exploitation of knowledge for 252 patients of a (HHU) within a Spanish regional hospital. The data collected was analyzed using the PLS-Graph. Findings To HHU managers, this study offers a set of guidelines to assist in their gaining an understanding of the role of counter-knowledge in organizational LPs and the potential contribution of communication technologies. Our findings support the proposition that the negative effects of counter-knowledge can be mitigated by using communication technologies. Originality/value It is argued in this paper that counter-knowledge may play a variety of different roles in the implementation of LPs. Specifically, the assignment of communication technologies to homecare units has given them the means to filter counter-knowledge and prevent users from any possible problems caused by such counter-knowledge.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".