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Record W2956840739 · doi:10.1136/ebnurs-2018-103020

Fragmented health and well-being services as contributing barriers in overcoming the unmet needs of a population with special needs

2019· letter· en· W2956840739 on OpenAlexaff
Nashit Chowdhury, Tanvir Chowdhury Turin

Bibliographic record

VenueEvidence-Based Nursing · 2019
Typeletter
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSpecial needsPopulationMedicineBusinessEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

Commentary on: Brewer A. “We were on our own”: Mothers’ experiences navigating the fragmented system of professional care for autism. Soc Sci Med 2018; 215 :61-8. Studies have shown that increasing fragmentation of the healthcare system leads to systemic deficiencies, lack of coordination between professionals from collaborating disciplines, increased expenditure, inequalities and deprofessionalisation.1 2However, information on how decentralisation or fragmentation affects healthcare receivers and their caregivers requiring multidisciplinary approach is limited. Brewer’s study examines how the subdivisions of professional jurisdictions of childhood disabilities between and within …

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.718
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.022
GPT teacher head0.347
Teacher spread0.325 · 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.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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