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Record W2770965581 · doi:10.5539/gjhs.v10n1p60

Do Clinical And Psychosocial Factors Affect Health-Related Quality of Life in Adolescents with Chronic Diseases?

2017· article· en· W2770965581 on OpenAlexvenueno aff
Teresa Santos, Margarida Gaspar de Matos, Adilson Marques, Celeste Simões, Isabel Leal, Maria do Céu Machado

Bibliographic record

VenueGlobal Journal of Health Science · 2017
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsPsychosocialQuality of life (healthcare)MedicineClinical psychologyDiseasePsychological resilienceSocial supportAffect (linguistics)PopulationGerontologyPsychologyPsychiatryEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Living with a chronic disease in adolescence can have an impact on the perception of Health-related Quality of Life (HRQoL). Facing the increasing relevance of psychosocial dimensions and also considering the interaction with clinical variables, this study aimed to measure the impact of clinical and psychosocial factors (separated and combined) on adolescent’s reported HRQoL.A cross-sectional study was conducted in a clinical population of 135 adolescents with chronic diseases (n=70 boys), average age: 14±1.5 years old. Through a self-reported questionnaire, HRQoL (KIDSCREEN-10), socio-demographic, clinical variables (diagnostic; time of diagnosis; self-perceived pain; disease severity proxy; disease-related medication intake/use of special equipment), and psychosocial variables (psychosomatic health; resilience; self-regulation; social support) were assessed.Separately, clinical and psychosocial variables showed a significant impact in HRQoL, 27.9% and 62.4%, respectively. Once combined, the previously identified variables had a significant impact (64.2%), but a different contribution from clinical and psychosocial variables was revealed: when first entering the clinical variables (model 1) the variance only reaches 30%, and much more from psychosocial variables seems to explain the total (64.2%); inversely, when first integrating psychosocial variables (model 2), the clinical ones added a small significance to the model (0.6%).The present study underlined the association of clinical (“disease-related”) and psychosocial (“non-disease-related”) factors on HRQoL. Furthermore, it reinforced the need to focus more on psychosocial dimensions, highlighted the potential role of psychosomatic health, resilience, self-regulation and social support. It can be suggested that the identification of impaired psychosocial domains may help professionals to better plan, and achieve effective interventions of psychosocial care.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.087
GPT teacher head0.472
Teacher spread0.385 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

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