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Record W2346879488 · doi:10.3928/02793695-20160420-04

Clinical Use of an Autovideography Intervention to Support Recovery in Individuals with Severe Mental Illness

2016· article· en· W2346879488 on OpenAlexaff
Sheila J. Linz, Nancy P. Hanrahan, Marissa DeCesaris, Ryan Petros, Phyllis Solomon

Bibliographic record

VenueJournal of Psychosocial Nursing and Mental Health Services · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsMental illnessMental healthPsychosocialThematic analysisIntervention (counseling)PsychologyReciprocity (cultural anthropology)NursingParticipatory action researchPsychiatryMedicineQualitative researchSocial psychology

Abstract

fetched live from OpenAlex

The current authors introduced an innovative autovideography intervention asking mental health consumers to use video cameras for 1 month to tell about their recovery. The research approach was based on a participatory research model with workers and consumers of a recovery education center fully involved with the study design and implementation. Twelve individuals who had graduated from a recovery program participated. The participant-produced videos were qualitatively analyzed using thematic analysis. The use of autovideography was found to be feasible and can be used clinically to support the process of recovery by providing opportunities for reciprocity, self-reflection, and advocacy. Consumer-produced videos provide a voice to inform others with and without mental illness about the concerns of individuals with mental illness and the process of recovery. [Journal of Psychosocial Nursing and Mental Health Services, 54(5), 33-40.].

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.363
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.102
GPT teacher head0.478
Teacher spread0.376 · 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.

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

Citations8
Published2016
Admission routes1
Has abstractyes

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