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Record W3212401238 · doi:10.2196/22140

Technology-Based Interventions for Cancer Caregivers: Concept Analysis

2021· article· en· W3212401238 on OpenAlex
Zhaohui Su, Xiaoshan Li, Dean McDonnell, Andrea A Fernandez, Bertha E. Flores, Jing Wang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Cancer · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionCINAHLPsycINFOContext (archaeology)CLARITYHealth technologyMedicineHealth carePsychologyNursingMEDLINEPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Cancer is a taxing chronic disease that demands substantial care, most of which is shouldered by informal caregivers. As a result, cancer caregivers often have to manage considerable challenges that could result in severe physical and psychological health consequences. Technology-based interventions have the potential to address many, if not all, of the obstacles caregivers encounter while caring for patients with cancer. However, although the application of technology-based interventions is on the rise, the term is seldom defined in research or practice. Considering that the lack of conceptual clarity of the term could compromise the effectiveness of technology-based interventions for cancer caregivers, timely research is needed to bridge this gap. OBJECTIVE: This study aims to clarify the meaning of technology-based interventions in the context of cancer caregiving and provide a definition that can be used by cancer caregivers, patients, clinicians, and researchers to facilitate evidence-based research and practice. METHODS: The 8-step concept analysis method by Walker and Avant was used to analyze the concept of technology-based interventions in the context of cancer caregiving. PubMed, PsycINFO, CINAHL, and Scopus were searched for studies that examined technology-based interventions for cancer caregivers. RESULTS: The defining attributes of technology-based interventions were recognized as being accessible, affordable, convenient, and user-friendly. On the basis of insights gained on the defining attributes, antecedents to, and consequences of technology-based interventions through the concept analysis process, technology-based interventions were defined as the use of technology to design, develop, and deliver health promotion contents and strategies aimed at inducing or improving positive physical or psychological health outcomes in cancer caregivers. CONCLUSIONS: This study clarified the meaning of technology-based interventions in the context of cancer caregiving and provided a clear definition that can be used by caregivers, patients, clinicians, and researchers to facilitate evidence-based oncology practice. A clear conceptualization of technology-based interventions lays foundations for better intervention design and research outcomes, which in turn have the potential to help health care professionals address the needs and preferences of cancer caregivers more cost-effectively.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.380
Teacher spread0.345 · 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