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The role of the Nurse Specialist in the highly specialized field of Mental Health and Deafness

2010· article· en· W2077854892 on OpenAlexaff
null Horne, J. Pennington

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2010
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsAlpha Technologies (Canada)
Fundersnot available
KeywordsPsychological interventionMental healthNursingField (mathematics)PsychologyMedical educationWork (physics)InterdisciplinarityMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Accessible summary • This paper considers the need for enhanced skills and knowledge to fulfil the role of a Specialist Nurse in the field of Mental Health & Deafness. • Informs professionals of a new group called the Mental Health & Deafness National Nurse Specialist Forum. • By enhancing the knowledge and skills of professionals in this specialized area of work will contribute to a high quality of assessment and treatment and attract staff in to an exciting, challenging and developing field. This opinion paper considers the need for enhanced clinical skills and knowledge to fulfil the role of a Specialist Nurse in the field of Mental Health & Deafness and informs professionals of a new group called the Mental Health & Deafness National Nurse Specialist Forum. Their knowledge and skills enable therapeutic interventions to be accessible and meaningful for Deaf people. A case study illustrates the complex nature of assessment and treatment in Mental Health & Deafness and highlights the potential devastating consequences that may occur if a Deaf person is misdiagnosed and does not access appropriate services. An increased awareness of the field and forum aims to increase the interest of nurses outside of the field and support a developing evidence base for Deaf sensitive interventions and opportunities for further pioneering work.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.423
Teacher spread0.406 · 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 designQualitative
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

Citations6
Published2010
Admission routes1
Has abstractyes

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