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Record W2968074902 · doi:10.1177/2333393619868979

Mothers’ Perceived Barriers to and Recommendations for Health Care Appointment Keeping for Children Who Have Cerebral Palsy

2019· article· en· W2968074902 on OpenAlexafffund
Marilyn Ballantyne, Laurie Liscumb, Erin Brandon, Janice Jaffar, Andrea Macdonald, Laura Beaune

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2019
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalUniversity of Toronto
FundersBloorview Research Institute
KeywordsAttendanceCerebral palsyRehabilitationQualitative researchNursingMedicineContext (archaeology)Health carePsychologyFamily medicinePhysical therapy

Abstract

fetched live from OpenAlex

Children with cerebral palsy (CP) require ongoing rehabilitation services to address complex health care needs. Attendance at appointments ensures continuity of care and improves health and well-being. The study's aim was to gain insight into mothers' perspectives of the factors associated with nonattendance. A qualitative descriptive design was conducted to identify barriers and recommendations for appointment keeping. Semi-structured interviews were conducted with 15 mothers of children with CP. Data underwent inductive qualitative analysis. Mothers provided rich context regarding barriers confronted for appointment keeping-transportation and travel, competing priorities for the child and family, and health services. Mothers' recommendations for improving the experience of attending appointments included virtual care services, transportation support, multimethod scheduling and appointment reminders, extended service hours, and increased awareness among staff of family barriers to attendance. The results inform services/policy strategies to facilitate appointment keeping, thereby promoting access to ongoing rehabilitation services for children with CP.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.243
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.123
GPT teacher head0.545
Teacher spread0.423 · 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

Citations34
Published2019
Admission routes2
Has abstractyes

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