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Record W4388866326 · doi:10.1177/17531934231214138

Mental health and psychosocial support for children with upper limb musculoskeletal conditions

2023· article· en· W4388866326 on OpenAlexafffund
Deanna Persaud, T Hutchison, Samantha J. Anthony, Kristen M. Davidge, Howard M. Clarke, Emily S. Ho

Bibliographic record

VenueJournal of Hand Surgery (European Volume) · 2023
Typearticle
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersUniversity of Toronto
KeywordsMedicinePsychosocialMental healthPsychological interventionStaffingHealth careNursingFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

This study describes mental health and psychosocial screening processes, access to care and interventions provided to children with upper limb musculoskeletal conditions. A cross-sectional e-survey study was conducted of 107 healthcare professionals who work with children with congenital hand and upper limb differences and brachial plexus birth injuries. Of them, 41 (38%) reported that they routinely screen for mental health and psychosocial concerns. Few (12%) reported the use of standardized outcome measures. In total, 51 (48%) healthcare professionals reported that there was a waiting list for mental health services at their institution. Collectively, healthcare professionals were unsatisfied with the staffing, access to care and types of interventions available. Reported barriers to care included the growing need for mental health support, lack of resources and poor continuity of care after referrals. Future research should focus on identifying and validating a mental health screening tool and investigating the processes affecting access to mental health care. Level of evidence: IV

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)0.000
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 score0.425
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.300
Teacher spread0.286 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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