Incorporation of the tidal model into the interdisciplinary plan of care – a program quality improvement project
Bibliographic record
Abstract
The Tidal Model was utilized in the development and implementation of an Interdisciplinary Care Plan (IPC) on an inpatient psychiatric unit at St. Joseph's Healthcare Hamilton, Canada. Recovery, client-centered care and interdisciplinary team function were important concepts that were also integrated in the creation of a new care plan. The Tidal Model honors the person and the person's story. This model focuses on engaging the person rather than an illness, with the goal of understanding the person's present situation and his or her relationship with health and illness. Recovery focuses on moving beyond illness, and client centred care acknowledges clients as participants in their own care. As a program quality improvement initiative, the impact of the new IPC was evaluated. Feedback demonstrated that the IPC had a positive impact on both client and caregiver satisfaction, provided a better understanding of the client's situation, and improved the team's ability to document a care plan that reflected the client's specific goals and the goals of the interdisciplinary team. The outcomes achieved through this project have provided support for the IPC along with the need for further evaluation. Our experience with the Tidal Model and the IPC has given voice to the patient's story and helped facilitate their journey towards recovery.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".