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Record W3004328714 · doi:10.1177/2167696820902291

Perceived Needs for Mental Health Care Among Emerging Adults With Cancer

2020· article· en· W3004328714 on OpenAlexaffabout
Jennie Tang, Christopher M. Perlman, Scott T. Leatherdale, Mark A. Ferro

Bibliographic record

VenueEmerging Adulthood · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMental healthMoodPsychiatryMedicineNormativeOddsGerontologyHealth carePsychologyLogistic regression

Abstract

fetched live from OpenAlex

The challenges of living with cancer can compound the normative stresses of navigating emerging adulthood and have long-term psychiatric consequences. However, there is a lack of information on the mental health services required by this potentially vulnerable group of young people. This study estimated the prevalence of mental disorder among emerging adults (EAs) with cancer and examined their perceived needs for mental health care. Data from 5,590 individuals (15–29 years) who participated in the Canadian Community Health Survey—Mental Health were used in the analyses. The prevalence of mood or substance use disorder among those with cancer ( n = 42) was 42.9% compared to 32.2% among controls. EAs with cancer had higher odds of reporting unmet perceived needs for mental health care, OR = 7.72, 95% CI [1.85, 28.57]. This suggests an opportunity to improve future health services aimed at addressing the mental health care needs for EAs with cancer and potentially, other long-term chronic conditions.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.304
Teacher spread0.288 · 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
Published2020
Admission routes2
Has abstractyes

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