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Record W4321459225 · doi:10.3389/fpsyt.2023.1030407

Return on investment from service transformation for young people experiencing mental health problems: Approach to economic evaluations in ACCESS Open Minds (Esprits ouverts), a multi-site pan-Canadian youth mental health project

2023· article· en· W4321459225 on OpenAlexafffundabout
Jai Shah, Zeinab Moinfar, Kelly K. Anderson, Hayley Gould, Daphne Hutt‐MacLeod, Philip Jacobs, Stephen Α. Mitchell, Thanh Nguyen, Rebecca Rodrigues, Paula Reaume‐Zimmer, Heather Rudderham, Sharon Rudderham, Rebecca Smyth, Shireen Surood, Liana Urichuk, Ashok Malla, Srividya N. Iyer, Éric Latimer

Bibliographic record

VenueFrontiers in Psychiatry · 2023
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsAlberta Health ServicesCanadian Mental Health AssociationUniversity of AlbertaDouglas CollegeMcGill UniversityInstitute for Clinical Evaluative SciencesWestern University
FundersCanadian Institutes of Health Research
KeywordsMental healthIntervention (counseling)Service (business)Health careMedicineIndigenousNursingBusinessPublic relationsEconomic growthPolitical sciencePsychiatryMarketingEconomics

Abstract

fetched live from OpenAlex

Introduction Mental health problems are common globally, and typically have their onset in adolescence and early adulthood—making youth (aged 11–25) an optimal target for prevention and early intervention efforts. While increasing numbers of youth mental health (YMH) initiatives are now underway, thus far few have been subject to economic evaluations. Here we describe an approach to determining the return on investment of YMH service transformation via the pan-Canadian ACCESS Open Minds (AOM) project, for which a key focus is on improving access to mental health care and reducing unmet need in community settings. Approach As a complex intervention package, it is hoped that the AOM transformation will: (i) enable early intervention through accessible, community-based services; (ii) shift care away toward these primary/community settings and away from acute hospital and emergency services; and (iii) offset at least some of the increased costs of primary care/community-based mental health services with reductions in the volume of more resource-intensive acute, emergency, hospital or specialist services utilized. Co-designed with three diverse sites that represent different Canadian contexts, a return on investment analysis will (separately at each site) compare the costs generated by the intervention, including volumes and expenditures associated with the AOM service transformation and any contemporaneous changes in acute, emergency, hospital or service utilization (vs. historical or parallel comparators). Available data from health system partners are being mobilized to assess these hypotheses. Anticipated results Across urban, semi-urban and Indigenous sites, the additional costs of the AOM transformation and its implementation in community settings are expected to be at least partially offset by a reduction in the need for acute, emergency, hospital or specialist care. Discussion Complex interventions such as AOM aim to shift care “upstream”: away from acute, emergency, hospital and specialist services and toward community-based programming which is more easily accessible, often more appropriate for early-stage presentations, and more resource-efficient. Carrying out economic evaluations of such interventions is challenging given the constraints of available data and health system organization. Nonetheless, such analyses can advance knowledge, strengthen stakeholder engagement, and further implementation of this public health priority.

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)0.092
metaresearch head score (Gemma)0.128
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.749

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0920.128
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.009
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0030.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.084
GPT teacher head0.402
Teacher spread0.317 · 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

Citations7
Published2023
Admission routes3
Has abstractyes

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