Bibliographic record
Abstract
PURPOSE: The paper aims to explore the barriers that currently exist to patient-driven treatment within the field of mental health care and reform. DESIGN/METHODOLOGY/APPROACH: This study represents action learning research using grounded theory to explore a possible causal basis for recidivism related to non-compliance with medication. Interviews addressed concerns from the literature around perceived barriers to patient-driven treatment evidenced by non-compliance with medically recommended pharmaceutical treatment. Results were correlated to look for emergent themes that were used to form the basis for subsequent interview questions. FINDINGS: An analysis of the resulting emergent themes illustrated the importance of participatory treatment and coaching rather than medically applied paternalistic care, which is seen as encouraging learned helplessness on the part of patients. Similar helplessness was also revealed in clinicians themselves. Patients' awareness of their own needs and demands for more services place clients and the caregivers at odds over appropriate care in an environment of limited resources. RESEARCH LIMITATIONS/IMPLICATIONS: The research was limited to only a small number of interviewees in one institution, all of whom were closely associated with mental illness in various capacities. The grounded theory nature of the research does, however, provide a framework for more research in other institutions to test and further explore some of the findings. PRACTICAL IMPLICATIONS: The study demonstrated a reinforcement of Maslow's theory of needs hierarchy. The study illustrated a step-wise approach to treatment to decrease the rate of failure and recidivism in mental health care. The provision of a stable living environment was viewed as instrumental in improving patients' compliance with pharmaceutical treatment. An action plan was therefore created to initiate the support of a transitional/emergency house by various community groups in partnership with pharmaceutical manufacturing companies. ORIGINALITY/VALUE: Recidivism in mental health-created by non-compliance in pharmaceutical treatment, is a major issue in Canada's health care system. This study brings to the forefront issues from a number of perspectives in order to form a course of action in response to its findings.
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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 | 0.032 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".