MétaCan
Menu
Back to cohort
Record W2303681998

Assessment of underreporting of suicide attempts treated in emergency departments in Montreal and suggestions for improvement

2006· article· en· W2303681998 on OpenAlexaboutno aff
C. Poulin, Janie Houle, Hugo Nieuwenhuyse

Bibliographic record

VenuePsychiatria Danubina · 2006
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Medical emergencyIncentiveReliability (semiconductor)Information systemHuman factors and ergonomicsSuicide preventionPoison controlPsychologyComputer scienceMedicineEngineering
DOInot available

Abstract

fetched live from OpenAlex

The goal of this study is to determine the potential of using hospital emergency department computer systems as tools to monitor suicide attempts treated in these emergency rooms. More specifically, the objectives are to draw up an inventory of the various computer systems used in hospitals and to evaluate the validity of the data collected, comparing them with information included in the patients' files. After completing a tour of emergency rooms in general hospitals, we put together a profile of the different computer systems used and verified the nature of the information contained in these systems concerning suicide attempts. We conducted a pilot project to estimate the scope of underreporting of suicide attempts by comparing data in the computer systems with that in patient files. Analysis of the utilisation of computer systems in hospitals revealed that the information recorded varies widely. Moreover, a comparison of the information available in these systems with that included in medical files shows that data validity and reliability are problematic. We suggest several strategies to improve the validity of data on suicide attempts, including training and increasing awareness of staff in emergency departments, and other incentive measures so that computer systems can be standardised. Language: en

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.362
Teacher spread0.333 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2006
Admission routes1
Has abstractyes

Explore more

Same venuePsychiatria DanubinaSame topicSuicide and Self-Harm StudiesFrench-language works237,207