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Record W4391511002 · doi:10.29011/2688-9501.101490

Factors Affecting the Psychiatric Patients’ Wait Time at Erie Shores Health Care: A Qualitative Enquiry

2023· article· en· W4391511002 on OpenAlexaffabout
Munira Sultana, Janisse Mr, Sharrow Jg

Bibliographic record

VenueInternational Journal of Nursing and Health Care Research · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsOntario Shores Centre for Mental Health SciencesUniversity of WindsorWestern University
Fundersnot available
KeywordsPsychiatryQualitative researchShoreHealth careMedicinePsychologySociologyPolitical scienceOceanographyLawSocial science

Abstract

fetched live from OpenAlex

Abstract Background: Erie Shores HealthCare (ESHC) is a rural, resource-poor, publicly funded, 72-bed hospital serving South-West Ontario region. Psychiatric patients’ timely access to care is an important indicator for the quality of care at the ESHC. This qualitative inquiry focused on the wait time of the psychiatric patients attending the ESHC emergency department. The primary objective was to interpret healthcare providers’ (informants) perspectives on possible factors affecting the wait time of the psychiatric patients, awaiting transfer or clinical care, at the hospital. Methods: A series of in-depth interviews were conducted with the informants following Thorne’s Interpretive Description approach. Thematic analysis was conducted to analyze and to interpret interview transcripts. Results: Emerged cross-cutting themes were: 1) admission process, 2) transfer process, 3) patient factors, 4) staff factors, and 5) available resources. Informants also recognized a knowledge gap in frontline workers on currently available mental health resources in the area. Conclusion: This qualitative inquiry has allowed Erie Shores HealthCare develop a one pager practical for frontline health workers in a rural setting to navigate the available community resources. Further research can validate our findings and may allow other rural hospitals to seek solutions to similar issues.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.304
GPT teacher head0.524
Teacher spread0.220 · 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.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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