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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 OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

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