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Record W3049627990 · doi:10.1186/s12888-020-02801-y

The needs and service preferences of caregivers of youth with mental health and/or addictions concerns

2020· article· en· W3049627990 on OpenAlexafffundabout
Roula Markoulakis, Samantha Chan, Anthony Levitt

Bibliographic record

VenueBMC Psychiatry · 2020
Typearticle
Languageen
FieldPsychology
TopicFamily Caregiving in Mental Illness
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook HospitalSunnybrook Health Science Centre
FundersSunnybrook Research Institute
KeywordsMental healthAnxietyDepression (economics)Help-seekingService (business)BachelorMedicineBachelor degreePsychiatryAddictionPsychologyService providerFamily medicineClinical psychologyNursingPolitical science

Abstract

fetched live from OpenAlex

Abstract Background Caregivers experience significant strains as a result of navigating the complex mental health and/or addiction (MHA) system for their youth with MHA issues. We examined the characteristics of Ontario families with youth with MHA issues and their service needs. Methods A cross-sectional survey study investigated the characteristics and service needs of families with youth with MHA issues across the province of Ontario, Canada. A total of 840 caregivers were recruited. Results 259 participants (Mage = 45.94, SD = 7.11) identified as caregiving for at least one youth with MHA issues. The majority of the participants were female (70.7%), married (73.4%), and completed at least some college/Bachelor degree (59.1%). The mean age of youth was 16.72 years (SD = 5.33) and the most frequently reported diagnoses were Depression (30.1%), ADHD (27.8%) and Generalized Anxiety Disorder (21.2%). Regression results demonstrated that presently accessing services, presently seeking services, and higher levels of barriers MHA services were significantly predictive of identifying navigation as helpful for finding appropriate MHA services (χ 2 (7) = 28.69, p < .001, Nagelkerke R 2 = .16). Furthermore, presently accessing services was significantly predictive of identifying case management as helpful (χ 2 (7) = 29.59, p < .001, Nagelkerke R 2 = .156), and of identifying a primary healthcare provider as helpful (χ 2 (7) = 38.75, p < .001, Nagelkerke R 2 = .197) for finding appropriate MHA services. Conclusion Identifying the nature and extent of youth MHA issues, service needs, and family preferences can inform the development of services that address families’ needs and lend vital support for accessing services within a complex system.

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)0.000
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 score0.703
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.049
GPT teacher head0.296
Teacher spread0.248 · 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

Citations20
Published2020
Admission routes3
Has abstractyes

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