MétaCan
Menu
Back to cohort
Record W4210418475 · doi:10.1111/jpm.12825

Understanding how Canadian healthcare providers have learned to identify co‐occurring PTSD symptoms and dementia in Veterans

2022· article· en· W4210418475 on OpenAlexaffabout
Kim Ritchie, Heidi Cramm, Alice Aiken, Catherine Donnelly, Catherine Goldie

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2022
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsDalhousie UniversityMcMaster UniversityQueen's University
Fundersnot available
KeywordsDementiaHealth carePsychiatryMEDLINEMedicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

WHAT IS KNOWN ON THE SUBJECT?: Little is known about how PTSD and dementia in Veterans is identified by health care providers. WHAT THE PAPER ADDS TO EXISTING KNOWLEDGE?: Healthcare providers identify those behavioural symptoms experienced by older people living with dementia that represent an unmet need associated with PTSD secondary to military service. Once healthcare providers recognize the presence of symptoms relevant to PTSD, they modify their care approach to include focused/tailored non-pharmacological care interventions that address environmental and situational variables that reflect military action. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Specialized education and training is needed to improve the identification of PTSD when existent with other co-occurring neurocognitive conditions such as delirium, dementia and depression. ABSTRACT: Introduction Co-occurring PTSD and dementia in Veterans can be difficult to distinguish from dementia-related responsive behaviours, which may result in inappropriate care management. Improved identification of PTSD and dementia is necessary to inform more appropriate and effective care for Veterans. Aim/Question The purpose of this study was to understand how Canadian healthcare providers have learned to identify the co-occurrence of PTSD symptoms in Veterans with dementia. Methods Eight semi-structured interviews employing the Critical Incident Technique were conducted with key informant healthcare providers who treat Veterans from across Canada. Framework analysis was used to code, sort and develop themes. Results Observed differences in Veterans with PTSD and dementia cued healthcare providers to seek our more information, leading to a new understanding of past trauma underlying the symptoms they observed. Healthcare providers then altered their usual care approaches to utilize trust-based and validation-oriented strategies resulting in more effective care management. Discussion Improvement in the identification of co-occurring PTSD and dementia in Veterans requires specialized education and training for healthcare providers. Implications for Practice Recognizing the complex needs of older Veterans with co-occurring PTSD and dementia is necessary for healthcare providers to implement more effective care for this population. Relevance Statement This paper provides mental health nurses with new understanding of co-occurring PTSD and dementia in Veterans. With an ageing Veteran population in Canada, mental health nurses need to be knowledgeable about the care for Veteran specific mental health needs.

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)0.012
metaresearch head score (Gemma)0.048
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 score0.136
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.048
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0160.006
Scholarly communication0.0060.004
Open science0.0030.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.155
GPT teacher head0.459
Teacher spread0.303 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Psychiatric and Mental Health NursingSame topicPosttraumatic Stress Disorder ResearchFrench-language works237,207