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Record W2905947591 · doi:10.22215/etd/2018-12659

Government Structure and Equity in Access to Psychotherapy: A Study of Canada, with Comparisons to Australia and the United Kingdom

2018· dissertation· en· W2905947591 on OpenAlexaffabout
Mary Bartram

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsCarleton University
Fundersnot available
KeywordsEquity (law)Mental healthGovernment (linguistics)Political scienceContext (archaeology)PopulationPublic administrationEconomic growthMedicineGeographyPsychiatryEnvironmental healthEconomicsLaw

Abstract

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Gaps in mental health funding and insurance coverage have resulted in significant unmet need and inequities in access.Both Australia and the United Kingdom have expanded public funding for psychotherapy over the past decade, and it remains to be seen how far the new federal transfer of $5 billion over ten years will go toward improving equity in access in Canada.This four-paper dissertation examines how the exclusion of psychotherapy came about in Canada and why it has persisted, the extent to which access currently depends on how rich or poor you are, and what could be done to change this in the Canadian context in light of lessons learned from Australia and the United Kingdom.The first paper analyses parliamentary debates to trace the role of Canada's decentralized government structure in constraining federal transfers.The evidence suggests that Canada's decentralized form of government has been at the heart of its inability thus far to introduce significant reforms.The primary contribution of other factors (such as stigma and cost) has been their influence over whether or not mental health has been enough of a national priority to warrant the use of federal spending power.The second and third papers are large-N studies using data from Canadian and Australian population health surveys to measure the extent to which access to psychotherapy and other mental health services varies by income.Income-based inequities in utilization and unmet need are found to be significant problems in Canada, particularly for psychologist services.In Australia, inequities in utilization are found to be less of a concern than in Canada, but unmet need for psychotherapy is more inequitable, suggesting a possible backlog effect with the expansion of public funding in 2006.The fourth paper uses interviews with key informants in Australia, the United Kingdom and Canada to delve more deeply into the relationship between government structure, service system design and equity in access to psychotherapy.The key finding is that while more centralized governments have greater capacity for reform, achieving equity in access requires explicit focus regardless of government structure, service system design or social insurance model.Writing a dissertation is a rather solitary endeavour and I could not have done it without support and encouragement from many quarters.First, I would like to thank my dissertation committee members: Allan Maslove, for his steady encouragement and insightful guidance from our first discussion to the completion of this dissertation; Jennifer Stewart, for inspiring the inequity research and for her invaluable assistance in puzzling it all through; and Vandna Bhatia, for strengthening the dissertation's approach to theory and for her excellent editorial suggestions.Thanks are also due to Saul Schwartz, for getting me off to a good start by asking so many fundamental questions during the proposal stage.This dissertation had its genesis in my work with the Mental Health Strategy team at the Mental Health Commission of Canada between 2008 and 2012.By focusing this dissertation on government structure and equity in access to psychotherapy, I was able to bring together policy questions from my earlier career as a family therapist with a deep curiosity about the role of jurisdictional dynamics in mental health policy.The mentors, friends and colleagues I was fortunate enough to work with at that time have continued to support and challenge my development as a researcher throughout the dissertation journey:

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)0.010
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 score0.161
Threshold uncertainty score0.974

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.014
Science and technology studies0.0160.004
Scholarly communication0.0060.002
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.173
GPT teacher head0.483
Teacher spread0.311 · 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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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