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Record W7055646696

Co-occurring Posttraumatic Stress Disorder Symptoms and Dementia in Veterans Living in Long-Term Care

2021· dissertation· en· W7055646696 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2021
Typedissertation
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaPsychological interventionHealth carePosttraumatic stressEtiologyQualitative research
DOInot available

Abstract

fetched live from OpenAlex

Background: In older Veterans with dementia, Posttraumatic Stress Disorder (PTSD) can emerge, re-emerge, or worsen, resulting in negative consequences for Veterans and their families. Once admitted to long-term care (LTC), health care providers can misattribute symptoms associated with the co-occurrence of PTSD and dementia to the behavioural and psychological symptoms of dementia, and this can result in inappropriate interventions. The purpose of this research is to explore how the co-occurrence of PTSD symptoms and dementia is understood and identified in Veterans living in LTC. Methods: This dissertation consisted of three studies. The first study was a scoping review that examined how the relationship between PTSD and dementia in Veterans has been described in the literature. In study two, multiple case studies were conducted to understand co-occurring PTSD symptoms and dementia in Veterans in two LTC facilities in Canada. The third study used a qualitative descriptive approach to explore how health care providers in Canada have learned to identify co-occurring PTSD symptoms and dementia in Veterans. Results: PTSD and dementia in Veterans is an emerging area of research that is focused on understanding the etiology underlying the relationship, the symptomatic expression of PTSD and dementia, and the implications on health care providers, treatment, and resources. Case studies revealed that co-occurring PTSD symptoms and dementia are recognized as different than dementia alone, but these differences are highly nuanced and result in greater care challenges. Effective interventions are tailored to address specific symptoms. Moreover, health care providers recognize differences in co-occurring PTSD iii symptoms and dementia from dementia alone and tailor their care approach to specific symptoms, instead of using usual care strategies for dementia. Conclusion: The results of this dissertation contribute foundational information about co-occurring PTSD and dementia symptoms, highlight important identification issues, inform potential interventions and educational needs of health care providers, and inform future research to enhance the identification of co-occurring PTSD symptoms and dementia in Veterans.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.208
Teacher spread0.201 · 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 designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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