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Record W3105659254 · doi:10.5334/ijic.5451

Experiences of Caregivers as Clients of a Patient Navigation Program for Children and Youth with Complex Care Needs: A Qualitative Descriptive Study

2020· article· en· W3105659254 on OpenAlexaffabout
Alison Luke, Kerrie E. Luck, Shelley Doucet

Bibliographic record

VenueInternational Journal of Integrated Care · 2020
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsSaint John Regional HospitalUniversity of New Brunswick
Fundersnot available
KeywordsNursingNeeds assessmentHealth careIntegrated careQualitative researchPsychologyMedicineMedical education

Abstract

fetched live from OpenAlex

The number of Canadian children and youth with complex care needs has continued to rise, and their need for resources across all sectors can be extensive. Navigating the maze of resources and services can create confusion and impact how care is delivered and integrated. Patient navigators can help support and guide patients and caregivers through the healthcare system by matching their needs to appropriate resources with the aim to improve access and promote the integration of care. This qualitative study explored caregivers' experiences caring for a child or youth with complex care needs, and their experiences and satisfaction as clients of a patient navigation centre. Participants included 22 clients from NaviCare/SoinsNavi, a patient navigation centre in Canada for children and youth with complex care needs and their families. Three main themes emerged: 1) caring for a child or youth with complex care needs, 2) navigating the system, and 3) the value of patient navigation. Findings suggest caregivers caring for a child or youth with complex care needs often feel overwhelmed, fearful, and alone; yet, patient navigation can be an innovative approach to support their needs through facilitating more convenient and integrated care, and improving access to education, supports, and resources.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.523

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 head0.038
GPT teacher head0.360
Teacher spread0.322 · 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

Citations35
Published2020
Admission routes2
Has abstractyes

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