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Record W2910357614 · doi:10.1002/hpm.2733

Who experiences unmet need for mental health services and what other barriers to accessing health care do they face? <scp>F</scp>indings from <scp>A</scp>ustralia and <scp>C</scp>anada

2019· article· en· W2910357614 on OpenAlexaboutno aff
Lisa Corscadden, Emily Callander, Stephanie M. Topp

Bibliographic record

VenueThe International Journal of Health Planning and Management · 2019
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
FundersJames Cook University
KeywordsMental healthFace (sociological concept)Health careInternet privacyNursingPsychologyMedicineComputer sciencePsychiatrySociologyPolitical science

Abstract

fetched live from OpenAlex

PURPOSE: To examine factors associated with unmet need for mental health services and links with barriers to access to care more broadly. METHODOLOGY: The Commonwealth Fund International Health Policy Surveys from 2013 and 2016 were used to explore factors associated with unmet need for adults who experienced emotional distress for 1320 respondents in Australia and 2284 in Canada. FINDINGS: Over one in five adults in Australia (21%) and in Canada (25%) experienced emotional distress, just over half said they received professional help (51% in Australia, 59% in Canada). The majority of those who did not get help indicated did not want to see a professional (37% in Australia, 30% in Canada). For those who did seek help, the factors associated with not receiving care included lower income, higher out-of-pocket health care costs, and poorer health. When compared with people with met needs, those with unmet needs for mental health services were more likely to also experience affordability, medication, and trust-related access barriers (AOR range 2.41 to 7.49 for the two countries, P < 0.01). CONCLUSION: Including unmet needs for mental health services as part of regular reporting on access to care may bring attention to access barriers for people with mental health 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.027
GPT teacher head0.373
Teacher spread0.345 · 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.

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

Citations43
Published2019
Admission routes1
Has abstractyes

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