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Record W3014154561

Mysteriet med de överrepresenterade decemberbarnen: En kvantitativ studie om sent födda barns överrepresentation inom neuropsykiatriska funktionsnedsättningar

2020· article· sv· W3014154561 on OpenAlexaboutno aff
Pontus Nilsson, Simon Karlsson

Bibliographic record

VenueLund University Publications Student Papers (Lund University) · 2020
Typearticle
Languagesv
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)PsychiatryPsychologyTest (biology)PediatricsMedicineDemographyClinical psychologySociology
DOInot available

Abstract

fetched live from OpenAlex

Children that are born later in the year are, based on previous studies, more likely to be diagnosed with a neuropsychiatric disorder (hereby NPD), which is a bit surprising considering that NPDs are considered to be primarily genetic disorders. The aim of our study is to test the hypothesis that there is a correlation between being born later in the year and an increased prevalence of referrals from school because of suspicion of NPD. We did this by looking at the connections between what time of the year children are born and the probability of being referred to psychiatric assessment due to suspicion of a neuropsychiatric disorder. The method we chose for our study was a quantitative survey sent out to school psychologists in the different municipalities in the region of Skåne, Sweden. The psychologists were asked to contribute with the birth month of the ten most recent children that had been referred to psychiatric assessment due to suspicion of NPD, as well as the specific diagnosis suspected. Our findings were not quite expected, considering the overrepresentation of children born late in the year among children diagnosed with NPD. On the one hand, we could see what we were looking for in our analysis because the number of children being referred actually increased somewhat throughout the year in a correlation analysis. But, on the other hand, the correlation was not as clear nor as strong as expected, and more interestingly, the last quarter of the year was the quarter with the least number of referred children, while we expected the opposite. Instead, it was the third quarter that contributed to the increase of referrals throughout the year in our analysis. In our discussion, we consider different factors that can affect this result. Factors such as regional differences in diagnostic guidelines and praxis or the fact that we only studied children referred to psychiatry from their school when there are numerous other ways for an investigation to be initiated. Or maybe the overrepresentation occurs in the diagnostic stage for some reason, rather than the referral stage.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.717
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.272
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2020
Admission routes1
Has abstractyes

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