Developing a model of recovery in mental health
Bibliographic record
Abstract
BACKGROUND: The recovery process is characterized by the interaction of a set of individual, environmental and organizational conditions common to different people suffering with a mental health problem. The fact that most of the studies have been working with schizophrenic patients we cannot extend what has been learned about the process of recovery to other types of mental problem. In the meantime, the prevalence of anxiety, affective and borderline personality disorders continues to increase, imposing a significant socioeconomic burden on the Canadian healthcare system and on the patients, their family and significant other 1. The aim of this study is to put forward a theoretical model of the recovery process for people with mental health problem schizophrenic, affective, anxiety and borderline personality disorders, family members and a significant care provider. METHOD AND DESIGN: To operationalize the study, a qualitative, inductive design was chosen. Qualitative research open the way to learning -- the inside -- about different perspectives and issues people face in their process of recovery. The study proposal is involving a multisite study that will be conducted in three different cities of the Province of Québec in Canada: Montréal, Québec and Trois-Rivières. The plan is to select 108 participants, divided into four comparison groups representing four types of mental health problem. Each comparison group (n = 27) will be made up of 9 units. Each unit will comprise one person with a mental health problem (schizophrenia, affective anxiety, and borderline personality disorders. Data will be collected through semi-structured open-ended interview. The in-depth qualitative analysis inspired from the grounded theory approach will permit the illustration of the recovery process. DISCUSSION: The transformation of our Health Care System and the importance being put on the people well-being and autonomy development of the person who are suffering with mental problem This study protocol follows-up on earlier theory-building process that begun with the work of Noiseux 2. The contribution of the present study is to increase the comprehension of the concept of recovery and to enhance the body of knowledge in that domain. Very few studies have examined recovery and the one that did used a descriptive approach which did not take into account the perspective of the family members and the caregivers of the recovery process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".