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Record W2951824377 · doi:10.4018/ijarphm.2019070101

Emotional Intelligence and Online Healthcare

2019· article· en· W2951824377 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Applied Research on Public Health Management · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategies and Innovation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInteroperabilityHealth careValue (mathematics)Healthcare systemPsychologyKnowledge managementPublic relationsComputer sciencePolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

As the North American healthcare system moves to online value-based care, the importance of engaging patients and families continues to intensify. However, simply engaging patients and families to improve their subjective satisfaction will not be enough for providers who want to maximize value. True optimization entails developing deep and long-term relationships with patients through understanding their needs. This article discusses the result of a research conducted in Canada. Out of 1100 questionnaires which were distributed, 850 valid returns were obtained. The collected data were analyzed using a SPSS 20.0 statistical. The findings indicate that IT healthcare is rapidly growing. However, despite a significant number of initiatives in Canada related to online health information, lack of interoperability remains one of the major challenges in implementing successful health IT systems at this time.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.860
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.190
GPT teacher head0.407
Teacher spread0.216 · 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