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Record W2611395461 · doi:10.1186/s12904-017-0203-2

Keep in Touch (KIT): feasibility of using internet-based communication and information technology in palliative care

2017· article· en· W2611395461 on OpenAlexaff
Qiaohong Guo, Beverley Cann, Susan McClement, Genevieve Thompson, Harvey Max Chochinov

Bibliographic record

VenueBMC Palliative Care · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsPalliative careLaptopThe InternetSocial connectednessNursingInternet privacyPsychologyQualitative propertyHealth careQualitative researchMedicineWorld Wide WebSocial psychologyComputer scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Confinement to an in-patient hospital ward impairs patients' sense of social support and connectedness. Providing the means, through communication technology, for patients to maintain contact with friends and family can potentially improve well-being at the end of life by minimizing social isolation and facilitating social connection. This study aimed to explore the feasibility of introducing internet-based communication and information technologies for in-patients and their families and to describe their experience in using this technology. METHODS: A cross-sectional survey design was used to describe patient and family member experiences in using internet-based communication technology and health care provider views of using such technology in palliative care. Participants included 13 palliative in-patients, 38 family members, and 14 health care providers. An iPad or a laptop computer with password-protected internet access was loaned to each patient and family member for about two weeks or they used their own electronic devices for the duration of the patient's stay. Quantitative and qualitative data were collected from patients, families, and health care providers to discern how patients and families used the technology, its ease of use and its impact. Descriptive statistics and paired sample t-tests were used to analyze quantitative data; qualitative data were analyzed using constant comparative techniques. RESULTS: Palliative patients and family members used the technology to keep in touch with family and friends, entertain themselves, look up information, or accomplish tasks. Most participants found the technology easy to use and reported that it helped them feel better overall, connected to others and calm. The availability of competent, respectful, and caring technical support personnel was highly valued by patients and families. Health care providers identified that computer technology helped patients and families keep others informed about the patient's condition, enabled sharing of important decisions and facilitated access to the outside world. CONCLUSIONS: This study confirmed the feasibility of offering internet-based communication and information technologies on palliative care in-patient units. Patients and families need to be provided appropriate technical support to ensure that the technology is used optimally to help them accomplish their goals.

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)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.181
GPT teacher head0.448
Teacher spread0.267 · 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 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

Citations28
Published2017
Admission routes1
Has abstractyes

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