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Record W2193431540 · doi:10.1017/s0714980815000392

Perception et réceptivité des proches-aidants à l’égard de la vidéosurveillance intelligente pour la détection des chutes des aînés à domicile

2015· article· en· W2193431540 on OpenAlexaff
Nolwenn Lapierre, Chloë Proulx Goulet, Alain St-Arnaud, Francine Ducharme, Jean Meunier, Sophie Turgeon Londei, Jocelyne Saint‐Arnaud, Francine Giroux, Jacqueline Rousseau

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2015
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsHôpital Maisonneuve-RosemontCentre de Santé et de Services Sociaux CavendishQuebec Rehabilitation Research NetworkUniversité de MontréalCentre de réadaptation Lethbridge-Layton-MackayComputer Research Institute of MontréalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsWorryPerceptionPsychologyPopulationApplied psychologyHumanitiesSocial psychologyMedicinePsychiatryArtEnvironmental health

Abstract

fetched live from OpenAlex

To address the issue of falls, which are increasing as the population ages, an intelligent video-monitoring system is being developed. The aim of the study is to explore caregivers' perceptions of and receptiveness to a prototype of this fall detection system. A cross-sectional mixed-method study was carried out with individual interviews of 18 caregivers. Statistical frequencies and content analysis were conducted (SPSS and N'Vivo). The results show that most participants (n = 15/18) liked the intelligent video-monitoring system and were willing to use it. They would worry less if they could be alerted if a care recipient fell, but they were concerned about privacy and cost. Participants had a positive perception of the system and expressed their wishes regarding the kind of alert and the person to contact in case of a fall.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.034
GPT teacher head0.260
Teacher spread0.227 · 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.

Study designOther design
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

Citations7
Published2015
Admission routes1
Has abstractyes

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