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Record W2316931140 · doi:10.3928/00989134-20140325-01

The Impact of an Acute Psychiatry Environment on Older Patients and Their Families

2014· article· en· W2316931140 on OpenAlexaboutno aff
Lillian Hung, Elizabeth Loewen, Buffy Bindley, Debra L. McLaren, Travis Feist, Alison Phinney

Bibliographic record

VenueJournal of Gerontological Nursing · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthActivities of daily livingPsychologyMedicineEthnographyNursingGerontologyPsychiatrySociology

Abstract

fetched live from OpenAlex

Ethnographic methods (observations and interviews) were used to investigate the physical environment of a geriatric psychiatry unit to understand how it meets the needs of patients with mental health conditions. Four interrelated themes of environmental qualities emerged as central in promoting healing: therapeutic, supportive of functional independence, facilitative of social connections, and personal safety and security. Therapeutic describes the existence of a home-like environment and quality sensory stimulations. Supportive of functional independence refers to the environmental features that make it easy for older adults to mobilize and perform activities of daily living. Facilitative of social connections indicates the provision of social spaces for patients, families, and staff to interact and engage in meaningful activities. Personal safety and security involves having staff in close proximity and minimizing disruptions from confused patients. The evidence suggests that the physical environment is important in making hospitals safe and supportive of healing for older adults with mental health conditions. [Journal of Gerontological Nursing, 40(9), 50–56.] Ms. Hung is doctoral student and Clinical Nurse Specialist, Ms. Loewen and Ms. Bindley are Clinical Nurse Specialists, Ms. McLaren and Mr. Feist are Registered Psychiatric Nurses, Providence Health Care (PHC), and Dr. Phinney is Associate Professor, University of British Columbia, Vancouver, British Columbia, Canada. The authors have disclosed no potential conflicts of interest, financial or otherwise. The authors thank PHC Research Challenge for funding this research. Address correspondence to Lillian Hung, MA, RN, Clinical Nurse Specialist, University of British Columbia, School of Nursing, T201-2211 Wesbrook Mall, Vancouver, BC, Canada V6T 2B5; e-mail: lhung@providencehealth.bc.ca. Received: October 04, 2013 Accepted: February 14, 2014 Posted Online: March 31, 2014

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)0.003
metaresearch head score (Gemma)0.009
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 score0.014
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0100.003
Scholarly communication0.0040.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.287
Teacher spread0.268 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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