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Record W4296354899 · doi:10.3390/healthcare10091790

Childhood Cancer and the Family: A Pilot Proposal for Comprehensive Intervention at the Time of Diagnosis

2022· article· en· W4296354899 on OpenAlexaboutno aff
Marta Mira-Aladrén, Javier Martín-Peña, Gemma Sevillano Cintora, Antonio Celma Juste, Marta Gil‐Lacruz

Bibliographic record

VenueHealthcare · 2022
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
FundersGobierno de Aragón
KeywordsIntervention (counseling)Childhood cancerProtocol (science)Quality (philosophy)DiseaseWork (physics)Quality of life (healthcare)MedicinePediatric cancerCancerPsychologyFamily medicinePsychiatryNursingAlternative medicineEngineeringPathology

Abstract

fetched live from OpenAlex

Childhood cancer has a great impact on children and their environment. To minimize this, countries such as Canada and the USA have protocols in the field of social work, although these are scarce in Europe and especially in Spain. This paper aims to develop a pilot protocol in Aragon (Spain) for the practice of onco-pediatric social work in one of the hardest moments: the diagnosis. For its elaboration, a previous study was carried out in three phases, which provided data on the disease and its impact on the family and children and a methodological basis for the intervention from social work, all considering the participation of the agents involved as a fundamental element. Variables have been identified that influence the impact on the family support network and its quality of life at the time of diagnosis of childhood cancer. In addition, different indicators have been explored, based on the reality of these families. Finally, a pilot proposal for a comprehensive family intervention protocol in the diagnosis of childhood cancer has been elaborated. This work is intended to be a guide for intervention and delimitation of quality standards to be considered when dealing with the diagnosis of childhood cancer.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.056
GPT teacher head0.361
Teacher spread0.305 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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