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Record W2786620956 · doi:10.3917/spub.176.0837

Conditions d’adoption du dossier de santé électronique personnel par les professionnels de la première ligne au Québec : perspectives professionnelle et organisationnelle

2018· article· fr· W2786620956 on OpenAlexaffabout
El Kebir Ghandour, Marie‐Pierre Gagnon, Jean‐Paul Fortin

Bibliographic record

VenueSanté Publique · 2018
Typearticle
Languagefr
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsCentres Intégré Universitaires de Santé et de Services SociauxCentre Intégré de Santé et Services Sociaux de Chaudière-Appalache
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

INTRODUCTION: We aimed to describe and analyse the factors and conditions influencing ePHR adoption by primary healthcare professionals for the follow-up and management of chronic diseases, as perceived by healthcare professionals and health organization managers. METHODS: A qualitative study was conducted in the context of an ePHR experimentation project in Quebec. In-depth semi-structured individual interviews were conducted with 11 professionals and three managers directly involved in ePHR implementation in a primary healthcare organization. RESULTS: The results highlight the emergence of themes comprising facilitators or barriers to ePHR adoption. The main factors identified were the clinicians' leadership and previous involvement in organizational transformations, the context of practice, technology maturity providing a useful, additional and relevant content, integration with the available clinical information systems facilitating two-way communication and supporting the development of patient-professional partnerships and patients' use and adherence. The organizational precursors identified refer to the organizational receptivity to change, adjustment to participants' values, and the policies and practices set up to support ePHR adoption by professionals and their patients. Cost is a major issue determining ePHR implementation. CONCLUSION: The factors and conditions identified will be useful strategically and operationally to design and implement new clinical and organizational practices and develop adapted technologies facilitating ePHR adoption by professionals.

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 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)0.013
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0080.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.373
Teacher spread0.355 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations4
Published2018
Admission routes2
Has abstractyes

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