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Record W3217436935

Using Tele-homecare to Improve Chronic Disease Monitoring

2013· article· en· W3217436935 on OpenAlexaboutno aff
Lise Lamothe, Marie-Andrée Paquette, Jean‐Paul Fortin, Françoise Labbé, Djamel Messikh, Julie Duplantie

Bibliographic record

VenueSante Publique · 2013
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelecareMedicineNursingKnowledge managementHealth careTelehealthTelemedicineAdaptation (eye)Medical emergencyProcess managementBusinessComputer sciencePsychology
DOInot available

Abstract

fetched live from OpenAlex

Aim: The purpose of this study was to understand how home telecare technologies can be used to improve services for people with chronic diseases.Methods: Canadian elders with at least one of the targeted chronic diseases (COPD, heart failure, hypertension, diabetes) were asked to use telehomecare equipment. The data needed to assess the implementation process and to monitor outcomes were collected through participatory observation, documentary analysis and interviews.Results: The study found that the technology has a number of benefits for patients, particularly in terms of access to health services. By enabling patients to access more information about their health, the use of the technology, combined with an educational program, contributes to increasing their capacity for self-management. The results also indicate that the telehomecare equipment had a positive impact on clinical decision-making. By facilitating health professionals’ access to information and expertise, it was found to promote interprofessional practice. The study found that telehomecare technology has an organizational impact on practice and requires organizational adaptation, the form of which will depend on local organizational and clinical settings.Conclusion: The results suggest that telehomecare technology helps to create conditions that need to be met by health care organizations in order to improve service delivery to people with chronic diseases, particularly with regard to interprofessional collaboration, health professionals’ access to information and expertise and active patient participation. However, the successful implementation of the technology requires a detailed analysis of the settings in which it is used.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

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

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 head0.040
GPT teacher head0.369
Teacher spread0.329 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2013
Admission routes1
Has abstractyes

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