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Record W2916578330 · doi:10.1108/jkm-10-2018-0636

Overcoming knowledge barriers to health care through continuous learning

2019· article· en· W2916578330 on OpenAlexaff
María Teresa Sánchez-Polo, Juan‐Gabriel Cegarra‐Navarro, Valentina Cillo, Anthony Wensley

Bibliographic record

VenueJournal of Knowledge Management · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsKnowledge managementHealth careAssimilation (phonology)BureaucracyBusinessComputer scienceProcess managementPolitical science

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to explore the role of continuous learning and the mitigation or elimination of knowledge barriers affecting information technology (IT) assimilation in the health-care sector. Most of the problems with IT assimilations stem from a poor understanding of the nature of suitable information, the lack of trust, cultural differences, the lack of appropriate training and hierarchical bureaucratic structures and procedures. To overcome these barriers, this study provides evidence that a continuous learning process can play a part in overcoming some of the obstacles to the assimilation of IT. Design/methodology/approach This study investigates how a continuous learning environment can counteract the presence of knowledge barriers, and, along with such an environment, can, in turn, facilitate IT assimilation. The study uses ADANCO 2.0.1 Professional for Windows and involves the collection and analysis of data provided by 210 health-care end users. Findings The study provides evidence in support of the proposition that continuous learning may facilitate the assimilation of IT by health-care end users through the mitigation of knowledge barriers (e.g. lack of trust or resistance to change). The mitigation of these barriers requires the gathering and utilization of new knowledge and knowledge structures. The results support the hypothesis that one way in which this can be achieved is through continuous learning (i.e. through assessing the situation, consulting experts, seeking feedback and tracking progress). Research limitations/implications A limitation of the study is the relatively simple statistical method that has been used for the analysis. However, the results provided here will serve as a preliminary basis for more sophisticated analysis which is currently underway. Practical implications The study provides useful insights into ways of using continuous learning to facilitate IT assimilation by end users in the health-care domain. This can be of use to hospitals seeking to implement end user IT technologies and, in particular, telemedicine technologies. It can also be used to develop awareness of knowledge barriers and possible approaches to mitigate the effects of such barriers. Such an awareness can assist hospital staff in finding creative solutions for using technology tools. This potentially augments the ability of hospital staff to work with patients and carers, encouraging them to take initiative (make choices and solve problems relevant to them). This, in turn, allows hospitals to avoid negative and thus de-motivating experiences involving themselves and their end users (patients) and improving IT assimilation. This is liable to lead to improved morale and improved assimilation of IT by end users (patients). Social implications As ICT systems and services should entail participation of a wide range of users, developers and stakeholders, including medical doctors, nurses, social workers, patients and programmers and interaction designers, the study provides useful social implication for health management and people well-being. Originality/value The paper contributes to a better understanding of the nature and impacts of continuous learning. Although previous studies in the field of knowledge management have shown that knowledge management procedures and routines can provide support to IT assimilation, few studies, if any, have explored the relationship between continuous learning and IT assimilation with particular emphasis on knowledge barriers in the health-care domain.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.064
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0060.003
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.018
GPT teacher head0.327
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations62
Published2019
Admission routes1
Has abstractyes

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