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Record W3080705305 · doi:10.3928/00989134-20200811-03

Family- and Person-Centered Interdisciplinary Telehealth: Policy and Practice Implications Following Onset of the COVID-19 Pandemic

2020· article· en· W3080705305 on OpenAlexaff
Abraham A. Brody, Tina Sadarangani, Tessa Jones, Kimberly Convery, Lisa Groom, Alycia A. Bristol, Daniel David

Bibliographic record

VenueJournal of Gerontological Nursing · 2020
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsYork University
FundersNational Institute of Nursing ResearchNational Institute on Aging
KeywordsTelehealthPandemicEquity (law)Coronavirus disease 2019 (COVID-19)NursingFamily centered care2019-20 coronavirus outbreakPublic relationsPsychologyTelemedicinePolitical scienceMedicineHealth careLaw

Abstract

fetched live from OpenAlex

With the onset of the COVID-19 pandemic, telehealth was thrust to the forefront, becoming one of the most predominant forms of care almost overnight. Despite years of research, practice, and policymaking, tenets for providing telehealth in an interdisciplinary, family- and person-centered fashion, and across a wide breadth of settings remain underdeveloped. In addition, although telehealth has the potential to increase equity in care, it can also further exacerbate disparities. The current article discusses the opening created by the pandemic and provides recommendations for how to make permanent changes in telehealth policy and practice to allow for interdisciplinary, person- and family-centered care while also taking care to address issues of equity and ethics and privacy issues related to telehealth and remote monitoring. [Journal of Gerontological Nursing, 46(9), 9-13.].

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)0.027
metaresearch head score (Gemma)0.085
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.085
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0090.012
Open science0.0020.008
Research integrity0.0150.013
Insufficient payload (model declined to judge)0.0090.001

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 head0.442
GPT teacher head0.525
Teacher spread0.083 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

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