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Record W2975941878 · doi:10.1177/1744629519873503

Snap shot: Achieving better care through a one-page personal health profile

2019· article· en· W2975941878 on OpenAlexaff
Megan Aston, Krista Sweet, Erin McAfee, Sheri Price, Jordan Sheriko, Joelle Monaghan, Jillian Filliter, Cathy Walls, Patrick J. McGrath, Emma Vanderlee, Amanda Bye

Bibliographic record

VenueJournal of Intellectual Disabilities · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsShot (pellet)Personal carePsychologyHealth careComputer scienceMedicineFamily medicinePolitical scienceMaterials science

Abstract

fetched live from OpenAlex

Children with intellectual disabilities (IDs) can have complex health conditions that require intense and ongoing care management by multiple healthcare professionals (HCPs). Families often experience frustrations and challenges sharing necessary information about their children's unique emotional and communicative needs with HCPs. In turn, these needs are often poorly documented and shared with other HCPs. This contributes to compromised care and frustrations for families and HCPs. We conducted a qualitative study using focus groups to examine how 10 parents and 3 HCPs experienced provision of care for children with ID, as well as their suggestions for developing a one-page personal health profile (PHP) to improve communication. Parents suggested including behavioural descriptors rather than diagnoses. All participants believed a one-page PHP that was child and parent led would be very helpful and would improve communication between HCPs, parents and children leading to effective and supportive 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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0180.001

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 head0.092
GPT teacher head0.413
Teacher spread0.321 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2019
Admission routes1
Has abstractyes

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