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Record W2889747517 · doi:10.2196/11717

Digital Solutions for Cancer Survivorship Care

2018· article· en· W2889747517 on OpenAlexvenueno aff
Ingrid Oakley‐Girvan, Sharon Davis, Michelle Longmire

Bibliographic record

VenueIproceedings · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsSurvivorship curveCancer survivorshipReceiptCancerCancer survivorHealth careMedicineFamily medicineGerontologyPsychologyNursingOncologyWorld Wide WebInternal medicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Background: Both the National Cancer Institute (NCI) and Institute of Medicine have stressed the importance of survivorship care plans (SCP) for cancer patients/survivors and discussed the significance and importance of required input from survivors and advocates. However, there are many barriers to cancer care coordination and the creation of SCPs, including oncology staff time required to write them. Although survivors valued SCPs and liked them, few survivors or caregivers report receiving survivorship information and in some studies, reported no receipt of an SCP. Digital platforms can support cancer survivorship care by integrating with the existing Electronic Health Record and presenting information in a dynamic and user-friendly format that improves coordination and communication. Objective: In this paper, we describe our involvement of stakeholders, including medical staff, patients/survivors and informal caregivers in developing a user-centered design for TOGETHERCARE, a smartphone app envisioned to provide critical functionality including planning and sharing of the SCP among survivors, physicians, and informal caregivers. Methods: Two interviewers conducted a total of nine semi-structured interviews, including a convenience sample of three health care providers who work with cancer patients, three cancer patients/survivors, and three informal caregivers currently caring for cancer patients/survivors. The interviews with Spanish-speaking patients/survivors and caregivers were conducted with a translator. Notes from the interviews were transcribed into a prepared template. The results were compiled and coded by two members of the core team. Results: We identified areas of consistency in responses between the three different groups in terms of how the application should work, as well as areas of difference. Additional suggestions for features for the application are also presented. Health care providers focused on the efficiency of using the application, features that would improve follow-up visits with patients and reduce the nursing triage, ER visits and readmissions. Survivors and caregivers were more focused on features that would provide assistance with patient appointment schedules, at-home medical tasks and activities of daily living. Although all three groups agreed that there is currently no systematic way for specialists to keep in touch with patients once they have moved to community care, and that SCPs would be useful, the practice of providing SCPs is rarely implemented. Survivors, caregivers, and providers all agreed that they have smartphones and that an app that includes the ability to communicate between the different groups, along with other features such as guidance on assisting with daily medical tasks and activities of daily living would be useful. Conclusions: The pervasiveness of mobile devices and mobile app use provides an opportunity to make survivorship information and plans more readily available to caregivers and survivors, and to incorporate patient outcome reporting. Health care providers, cancer survivors, and informal caregivers all responded positively to a variety of features that could improve the efficiency of cancer care coordination and dynamic SCP provision.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

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

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 head0.309
Teacher spread0.270 · 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 designNot applicable
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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