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Record W4377221170 · doi:10.3389/fpsyg.2023.1154657

Analyzing the use of videoconference by and for older adults in nursing homes: an interdisciplinary approach to learn from the pandemic

2023· article· en· W4377221170 on OpenAlexaff
Céline Racin, Raphaël Minjard, Christophe Humbert, Vivien Braccini, Fabien Capelli, Cédric Sueur, Célia Lemaire

Bibliographic record

VenueFrontiers in Psychology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversité Laval
FundersUniversité de StrasbourgAgence Nationale de la Recherche
KeywordsPsychicPsychologyFeelingVideoconferencingMediationAffect (linguistics)Isolation (microbiology)Social psychologyCognitionSocial isolationQuality (philosophy)NursingSociologyMedicinePsychotherapistComputer scienceCommunicationPsychiatry

Abstract

fetched live from OpenAlex

Introduction: During the Covid-19 pandemic and the resulting visitation restrictions, digital tools were used in many nursing homes in France to allow the older adults and their relatives to maintain social contact via videoconferencing. This article adopts an interdisciplinary approach to analyze the processes that affect the use of digital technologies. Methods: Drawing on the concept of "mediation," it seeks to shed light on how individuals embrace these tools in a relational situation. The interviews and observations undertaken among residents, their relatives, professionals, and the management head of seven nursing homes in 2021, make it possible to outline the different forms of practices and uses and to identify the factors leading to the variations observed. Results: While the key objective of these technical and technological tools is to compensate - on a functional level - for the communication problems and the isolation of individuals in order to promote residents' "quality of life" by maintaining "social contact," our study reveals that these tools' uses and practices largely differ. It also shows considerable inequalities in terms of residents' acquisition of subjective feelings of ownership of the tools. These are never attributed to isolated physical, cognitive, psychic, and social difficulties, but are influenced by specific organizational, interactional, and psychic configurations. Some of the structures analyzed revealed situations in which mediation failed, occasionally exposing the risk associated with seeking "ties at all costs," or revealing a disturbing strangeness when residents were placed in front of screens. Some configurations, however, showed that it was possible to set up an intermediate space for the experience to unfold, which in turn opened up a space where individuals, groups, and institutions could experiment, allowing them to develop subjective feelings of ownership of this experience. Discussion: This article discusses how the configurations that failed to promote the mediation process reveal the need to assess the representations of care and assistance in the relationships between older adults, their loved ones, and nursing home professionals. Indeed, in certain situations, the use of videoconferencing, while seeking to produce a positive effect, risks displacing and increasing the effects of the "negative" associated with dependency, which may worsen individuals' difficulties within nursing homes. The risks associated with the failure to take into account residents' requests and consent explain why it is important to discuss how certain uses of digital tools may renew the dilemma between concerns for protection, on the one hand, and respect for autonomy on the other.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.080
GPT teacher head0.445
Teacher spread0.365 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations9
Published2023
Admission routes1
Has abstractyes

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