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Family‐centred care and health‐related quality of life of patients in paediatric neurosciences

2009· article· en· W2064706287 on OpenAlexaff
Mel Moore, Jean K. Mah, Barry Trute

Bibliographic record

VenueChild Care Health and Development · 2009
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsPsychosocialIllness severityQuality of life (healthcare)MedicineMultilevel modelExploratory researchPerceptionSeverity of illnessClinical psychologyPsychiatryPsychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Little is known about the influence of contextual factors such as health services characteristics on health-related quality of life (HRQL) for children with a neurological condition. To address this gap, we conducted an exploratory study of the relationship between family-centred care (FCC) and HRQL outcomes in children from neurosciences clinics in a large acute care hospital. METHODS: A total of 187 family caregivers completed questionnaires regarding their socio-demographic status, the severity of their children's condition (FIM), perceptions of their children's HRQL (PedsQL 4.0) and their experiences of FCC (MPOC-20). Hierarchical regression analyses explored the hypothesis that FCC is a significant predictor of children's HRQL, independent of illness severity. RESULTS: Illness severity and FCC jointly explained one-third of the variance in children's total HRQL. When FCC was controlled for illness severity, it remained a significant predictor of physical, psychosocial and total HRQL scores. CONCLUSIONS: This study provides evidence that the level of FCC is positively related to paediatric HRQL independent of neurological illness severity. The implication is that the uptake of FCC practices by service providers can positively impact the quality of life of children with neurological disorders.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.042
GPT teacher head0.320
Teacher spread0.278 · 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 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

Citations37
Published2009
Admission routes1
Has abstractyes

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