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Record W2586230104 · doi:10.7748/ncyp.2017.e814

How professionals should communicate with children who have mental healthcare needs

2017· article· en· W2586230104 on OpenAlexaff
Rachael Bolland, Rebecca Calnan

Bibliographic record

VenueNursing Children and Young People · 2017
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsNetwork for Business Sustainability
Fundersnot available
KeywordsHealth professionalsMental healthMental healthcareMedical educationNursingHealth carePsychologyGeneralist and specialist speciesMedicinePsychiatry

Abstract

fetched live from OpenAlex

Young people with mental health needs are often cared for on children's wards by generalist children's healthcare professionals (CHCPs). Generalist CHCPs find these encounters challenging and difficult but they are viewed as an opportunity to improve the healthcare offered to these young people. The authors secured funding from Health Education South London to design and deliver interactive workshops to improve the communication skills of CHCPs with adolescents in challenging circumstances. In this article, the authors outline the design and content of the workshops and discuss how the workshops explore and challenge the attitudes the participants have that could prevent a young person from seeking support or engaging with professionals. They also describe how the workshops have improved generalist CHCPs' confidence and communication skills when talking with young people and how participants now use these encounters as an opportunity to improve healthcare for children and young people.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.025
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0060.004
Scholarly communication0.0050.005
Open science0.0010.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0060.002

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 head0.054
GPT teacher head0.403
Teacher spread0.349 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations7
Published2017
Admission routes1
Has abstractyes

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