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Record W2617070512 · doi:10.1111/jpm.12405

Staff perspectives: What is the function of adult mental health day hospital programs?

2017· article· en· W2617070512 on OpenAlexaffabout
Marlene Taube‐Schiff, Megan Ruhig, Adrienne Mehak, Melanie Deathe van Dyk, Stephanie E. Cassin, Thomas Ungar, David Koczerginski, Sanjeev Sockalingam

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychiatric care and mental health services
Canadian institutionsToronto Western HospitalWilliam Osler Health SystemUniversity of TorontoUniversity Health NetworkToronto Metropolitan UniversityNorth York General Hospital
Fundersnot available
KeywordsMental healthNursingMedicineCurriculumMental illnessCoping (psychology)PsychiatryPsychologyMedical educationPedagogy

Abstract

fetched live from OpenAlex

WHAT IS KNOWN ON THE SUBJECT?: Psychiatric day hospital (DH) treatment has been offered since the 1930s and is appropriate for individuals experiencing intense psychiatric symptoms without requiring 24-hour inpatient care. No empirical research has examined the specific purpose of DH treatment from the perspectives of healthcare providers within these programs. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: This study was the first to address the question of the purpose and function of DH treatment from the outlook of frontline workers within this setting, and confirmed anecdotal observations that DH treatment provides an alternative to intensive psychiatric care, and also operates as "bridge" between these intensive services and purely outpatient treatment. Additional information emerged, such as the importance of the name of DH programs avoiding connotations of illness, the benefits and skills that draw patients to these programs, and challenges that staff and patients experience within DH programs (e.g. short length of treatment, barriers to treatment access). WHAT ARE THE IMPLICATIONS FOR PRACTICE?: This information can enhance curriculum development within these settings. For example, given the importance of skill building, it is essential to integrate the provision of skill building and coping strategies within these settings. In addition, given that the name of the setting can impact staff (and perhaps service users as well), ensuring that the name of such program highlight wellness and recovery may enable a different type of therapeutic community to develop within these settings. ABSTRACT: Introduction Despite the benefits of psychiatric day hospitals (DH), research has not addressed staff perspectives of these programs' effectiveness and barriers. Aim To elucidate staff perceptions of Adult Mental Health DH programs at two hospitals in Canada, allowing for improved programming, enhanced structure and increased understanding of DH settings within the continuum of care. Method Twenty-five DH staff members completed semi-structured qualitative interviews. Two independent coders applied content analysis to achieve data saturation. Results Four major themes emerged: (1) program purpose and function, (2) what is in a name, (3) perceived patient motivation, and (4) room for improvement. Discussion Findings highlighted the importance of a multidisciplinary team delivering education and skill-focused interventions. Services were cited as "bridging" different mental health settings. Challenges included barriers to treatment access and inadequate length of treatment. Implications for Practice Understanding the function and purpose of this treatment service may enhance service delivery by enabling programs to integrate identified key ingredients. Providers can also note treatment duration and consider how to best use that time. Finally, language used within a DH setting appears to impact staff delivering services, and may also alter patients' understanding of the services they will receive and purpose of the program.

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)0.009
metaresearch head score (Gemma)0.022
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0060.008
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.363
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 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
Published2017
Admission routes2
Has abstractyes

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