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Record W2762156710 · doi:10.1177/0009922817734361

Experiences of Inner-City Fathers of Children With Chronic Illness

2017· review· en· W2762156710 on OpenAlexaff
Anna Kobylianskii, Thivia Jegathesan, Elizabeth Young, Kimmy Fung, Joelene Huber, Ripudaman Minhas

Bibliographic record

VenueClinical Pediatrics · 2017
Typereview
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineInner cityPediatricsFamily medicineEnvironmental planning

Abstract

fetched live from OpenAlex

We aim to explore the experiences of fathers from inner-city families caring for children affected by chronic health conditions or disabilities. A systematic scoping review was conducted using the Arskey and O'Malley framework. Fourteen of the 5114 articles were included in the full review and were qualitatively evaluated in terms of stressors, resources, perception, coping, and adaptation according to the Double ABCX model. Stressors included financial strain and health care access barriers. Resources ranging from immediate to extended family members depended on ethnicity. Fathers' perceptions of their primary caregiver roles depended on ethnicity in the context of cultural gender norms. While inner-city fathers desired information about their children's health, some were uncomfortable asking physicians. They had a higher risk for coping difficulties and maladaptation, including depression. We highlight a need for pediatricians to advocate for additional resources to provide comprehensive care for inner-city fathers caring for their children with chronic health conditions or disabilities.

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.254
GPT teacher head0.519
Teacher spread0.266 · 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 designQualitative
Domainnot available
GenreReview

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

Citations10
Published2017
Admission routes1
Has abstractyes

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