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Record W2810895383 · doi:10.1111/jpm.12489

Factors affecting mental health nurses working with clients with first‐episode psychosis: A qualitative study

2018· article· en· W2810895383 on OpenAlexaff
Constance Odeyemi, Jean Morrissey, Gráinne Donohue

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2018
Typearticle
Languageen
FieldPsychology
TopicFamily Caregiving in Mental Illness
Canadian institutionsTrinity College
Fundersnot available
KeywordsMental healthPsychologyNursingIsolation (microbiology)PsychiatryMedicine

Abstract

fetched live from OpenAlex

WHAT IS KNOWN ABOUT THE SUBJECT?: First-episode psychosis (FEP) usually occurs in adolescence, a time of great change and upheaval and the effect on the sufferer and their family can be immense The nurse's role is to alleviate this suffering, aid recovery and minimize the risk of relapse. They manage this onerous task ideally through the therapeutic relationship, and use the skills of assessment and risk identification in order to maximize patient outcomes. WHAT DOES THE STUDY ADD TO EXISTING KNOWLEDGE?: The study adds knowledge about the challenges that mental health nurses experience specifically in the presentation of first-episode psychosis The findings of this study reinforce the idea that pathways to care need to be clearly identified with a community-wide educational led experience This study illuminates the fact that additional training and formalized clinical supervision are necessary for mental health nurses to improve quality of care and reduce stress levels, both of which lead to better clinical outcomes. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Mental Health nurses should engage with additional training, formalized clinical supervision and avail of peer support in order to improve confidence, skills and quality of care. Dialogue among mental healthcare colleagues is important not only about caring for people presenting with a first-episode psychosis but in relation to the wider community and family. This demonstrates the need for family-centred care within the mental health profession. There should be more recognition of the social impact on the individual during untreated psychosis with regard to isolation and withdrawal as well as factors which also affect help-seeking behaviours. ABSTRACT: Introduction Although there is much research on mental health nurses working with individuals presenting with psychosis, there is a lack of knowledge about the factors that impact the experience of nurses in the presentation specifically of first-time psychosis. Aim This study aimed to explore the factors that impact on the experience of mental health nurses working with individuals and their families who present with a first-time psychosis. Method This qualitative study was conducted through individual semi-structured interviews with eight mental health nurses recruited from community mental health settings with a minimum of 2 years post-qualification experience. Data were then subjected to a thematic content analysis. Results This study identified the importance of therapeutic engagement, as well as the need to have clear pathways to care and building capacity through clinical supervision and training when working with this population. Implications for practice Mental Health nurses should engage with additional training, formalized clinical supervision and avail of peer support in order to improve confidence, skills and quality of care, leading to better therapeutic engagement. Pathways to care should be embedded within the wider community to ensure ease of access for individuals and their families. There should be more recognition of the social impact on the individual during untreated psychosis with regard to isolation and withdrawal as well as factors which also affect help-seeking behaviours.

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)0.013
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.026
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0050.004
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.418
Teacher spread0.370 · 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 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".

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Citations16
Published2018
Admission routes1
Has abstractyes

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