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Record W4383220057 · doi:10.7202/1100664ar

STAYING CONNECTED: SERVICE-USER EXPERIENCE OF THE RECOVERY JOURNEY AND LONG-TERM ENGAGEMENT WITH A MENTAL HEALTH CLINIC

2023· article· en· W4383220057 on OpenAlexvenueno aff
Lyuda Krupin, Nick Todd, Eric Howey, Tara Perry

Bibliographic record

VenueCanadian social work review · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthService (business)PsychologyMental health serviceService delivery frameworkTerm (time)Sample (material)Order (exchange)NursingApplied psychologySocial psychologyMedicinePsychotherapistBusinessMarketing

Abstract

fetched live from OpenAlex

While there has been much interest in recent years about the potential impact that short-term therapy can have on those needing mental health support, relatively little attention has been paid to the needs of those who require long-term support. In this phenomenological study exploring long-term service-users’ experiences of the recovery journey and the role of mental health support in facilitating that journey, a sample ( n = 6) of service-users who had a minimum of five years of continuous involvement with a community-based mental health clinic participated in a pair of focus groups designed to help them share their experience of the recovery journey. Our analysis revealed themes of contending not just with extreme violence and other adversities, but also with an often unhelpful helping system, as service-users expended effort in locating the consistent, accessible support they needed in order to find a reason to go on in the wake of devastating personal experiences. The study also emphasized how prioritizing the top-down need for efficiency over the bottom-up need for consistent, flexible support can have the inadvertent effect of extending rather than shortening treatment. Implications of these findings for the delivery of mental health services are discussed.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.289
GPT teacher head0.456
Teacher spread0.167 · 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 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
Published2023
Admission routes1
Has abstractyes

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