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Record W1994396623 · doi:10.1177/0883073813511857

What Do Patients and Families Want From a Child Neurology Consultation?

2013· article· en· W1994396623 on OpenAlexaff
Joseph M. Dooley, Kevin Gordon, Paula Brna, Ellen Wood, Ismail Mohamed, Erin M. Macdonald, Caitlin S. Jackson-Tarlton

Bibliographic record

VenueJournal of Child Neurology · 2013
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsWorryPediatric NeurologyPsychologyNothingFamily medicineNeurologySick childPsychiatryMedicinePediatricsAnxiety

Abstract

fetched live from OpenAlex

Understanding what patients and their parents want is essential to plan appropriate patient-centered care. Questionnaires were distributed to 500 consecutive children and parents seen for their first pediatric neurology consultation. Both patients and their families answered questions about their expectations of the consultation, their level of worry, and the Penn State Worry Questionnaire. The 5 most important issues for the parents were to get information, to work with the doctor to manage the problem, to have questions answered, to find out what was wrong, and to discuss the impact on the child's life. The children had very similar priorities. The 5 least important concerns for parents were to get a prescription, blood tests, to talk to others with similar problems, to get a radiograph/computed tomography/magnetic resonance imaging (MRI) and to be told nothing is wrong. The pediatric neurologists did well in anticipating these priorities but had more difficulty appreciating parent and patient level of worry.

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)0.003
metaresearch head score (Gemma)0.021
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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
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

Citations5
Published2013
Admission routes1
Has abstractyes

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