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Record W2907182913 · doi:10.2196/11516

Sensor-Based Passive Remote Monitoring and Discordant Values: Qualitative Study of the Experiences of Low-Income Immigrant Elders in the United States

2019· article· en· W2907182913 on OpenAlexvenueno aff
Clara Berridge, Keith Chan, Youngjun Choi

Bibliographic record

VenueJMIR mhealth and uhealth · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)WorkforceImmigrationQualitative researchPopulationAging in placeSociocultural evolutionGerontologyBusinessMedicinePsychologySociologyGeographyEconomic growthEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Remote monitoring technologies are positioned to mitigate the problem of a dwindling care workforce and disparities in access to care for the growing older immigrant population in the United States. To achieve these ends, designers and providers need to understand how these supports can be best provided in the context of various sociocultural environments that shape older adults' expectations and care relationships, yet few studies have examined how the same remote monitoring technologies may produce different effects and uses depending on what population is using them in a particular context. OBJECTIVE: This study aimed to examine the experiences and insights of low-income, immigrant senior residents, family contacts, and staff of housing that offered a sensor-based passive monitoring system designed to track changes in movement around the home and trigger alerts for caregivers. The senior housing organization had been offering the QuietCare sensor system to its residents for 6 years at the time of the study. We are interested in adoption and discontinuation decisions and use over time, rather than projected acceptance. Our research question is how do cultural differences influence use and experiences with this remote monitoring technology? The study does not draw generalizable conclusions about how cultural groups interact with a given technology, but rather, it examines how values are made visible in elder care technology interactions. METHODS: A total of 41 participants (residents, family, and staff) from 6 large senior housing independent living apartment buildings were interviewed. Interviews were conducted in English and Korean with these participants who collectively had immigrated to the United States from 10 countries. RESULTS: The reactions of immigrant older adults to the passive monitoring system reveal that this tool offered to them was often mismatched with their values, needs, and expectations. Asian elders accepted the intervention social workers offered largely to appease them, but unlike their US-born counterparts, they adopted reluctantly without hope that it would ameliorate their situation. Asian immigrants discontinued use at the highest rate of all residents, and intergenerational family cultural conflict contributed to this termination. Social workers reported that none of the large population of Russian-speaking residents agreed to use QuietCare. Bilingual and bicultural social workers played significant roles as cultural navigators in the promotion of QuietCare to residents. CONCLUSIONS: This research into the interactions of culturally diverse people with the same monitoring technology reveals the significant role that social values and context play in shaping how people and families interact with and experience elder care interventions. If technology-based care services are to reach their full potential, it will be important to identify the ways in which cultural values produce different uses and responses to technologies intended to help older adults live independently.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.402
Teacher spread0.363 · 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 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

Citations32
Published2019
Admission routes1
Has abstractyes

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