Assessing capacity in the complex patient: RCAT's unique evaluation and consultation model.
Bibliographic record
Abstract
Abstract This paper describes the development of a unique multidisciplinary patient assessment team, the Regional Capacity Assessment Team (RCAT), which operates in the Calgary Health Region of Alberta. The goals of this paper are to provide a brief review of seminal models that influenced RCAT's development, discuss its ethical and theoretical underpinnings, and provide an overview of the RCAT approach to the completion of complex assessments. The overview of the RCAT model will elucidate our multidisciplinary assessment algorithm, our consultation model, and describe our specialized assessment tools. This paper will be of interest to health care practitioners and administrators looking for a cost-effective, efficient, and clinically sound model for complex assessments. Capacity issues are challenging for everyone involved, due to the multiple legal, ethical, and medical factors involved. The Regional Capacity Assessment Team (RCAT) is currendy the only multidisciplinary team in Canada with the sole function of assessing and addressing issues. It was created in 2005 to address the multiple complexities of evaluations. RCAT serves the Calgary Health Region (CHR), which has a geographic area of 39,260 square kilometres and which serves a population of 1,143,368 (CHR, 2004). The CHR's main source of revenue (87%) is Alberta Health and Wellness (CHR, 2004). RCAT's assessment and consultation mandate includes the provision of services to all adults who have reached the age of majority, with no exclusionary criteria related to diagnosis. RCAT's patients have included adults with dementia, brain injury, psychiatric illness, addiction, and developmental disability. Frequently, there are co-morbid diagnoses, multiple medical conditions, and significant psychosocial stressors present. RCAT assessments are conducted across the CHR care spectrum, which includes acute care, community, and continuing care sites. Capacity is a socio-legal construct frequently used in the health care domain in relation to decisions about a patient's medical and social care (Weisstub, 1990). Although definitions of vary by jurisdiction, RCAT conceptualizes as the ability to use cognitive processes to understand and identify options, to appreciate the consequences of different options, and to follow through (or direct a surrogate to follow dirough) with chosen options. This conceptualization of capacity, especially as it incorporates understanding and appreciation, reflects current practice and literature in the field (Grisso & Appelbaum, 1998; Office of the Public Guardian and Trustee & Ontario Ministry of the Attorney General, 2000; Weisstub, 1990; Yukon Department of Justice, 2004). Current clinical practice also differentiates between capacity and competency. The term capacity is used in relation to the clinical assessment and determination of a patient's decision-making abilities by health care professionals. By contrast, the term competency refers to the court's decision and ruling regarding the clinical determination of (Brody, 2005; Checkland & Silberfeld, 1995; Lapid et al., 2003; Sturman, 2005; Sullivan, 2004; Wyszynski & Garfein, 2005). Seminal Capacity Models in the Literature At the inception of RCAT, and approximately four months prior to opening our clinic doors, an extensive review of existing models representative of North America, Europe, Asia, and Australia was completed. For brevity, this paper will highlight five key Canadian models and approaches that particularly influenced the conceptualization and development of our team. Baycrest Competency Clinic (Ontario) The Competency Clinic for the elderly (now closed) at the Baycrest Centre in Toronto incorporated a multidisciplinary team, with a primary goal of developing and refining criteria of (Silberfeld et al., 1988). …
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".