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Record W2737300157 · doi:10.1177/2333393617721646

Care Experiences of Adults With a Dual Diagnosis and Their Family Caregivers

2017· article· en· W2737300157 on OpenAlexafffund
David Nicholas, Avery Calhoun, Anne-Marie McLaughlin, Janki Shankar, Linda Kreitzer, Masimba Uzande

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2017
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsUniversity of Calgary
FundersAlberta Health Services
KeywordsDual (grammatical number)PsychologyGerontologyMedicineArt

Abstract

fetched live from OpenAlex

Individuals diagnosed with developmental disability and mental illness (a “dual diagnosis”) contend with multiple challenges and system-related barriers. Using an interpretive description approach, separate qualitative interviews were conducted with adults with a dual diagnosis ( n = 7) and their caregiving parents ( n = 8) to examine care-related experiences. Results indicate that individuals with a dual diagnosis and their families experience misunderstanding and stigma. Families provide informal complex care amid insufficient and uncoordinated services but are often excluded from formal care planning. A lack of available funding and services further impedes care. While negative care experiences are reported as prevalent, participants also describe instances of beneficial care. Overall, findings indicate a lack of sufficiently targeted resources, leaving families to absorb system-related care gaps. Recommendations include person- and family-centered care, navigation support, and capacity building. Prevention and emergency and crisis care services, along with housing, vocation, and other supports, are needed. Practice and research development regarding life span needs are recommended.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.002
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 head0.168
GPT teacher head0.528
Teacher spread0.360 · 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

Citations17
Published2017
Admission routes2
Has abstractyes

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