A qualitative descriptive study on the alignment of care goals between older persons with multi-morbidities, their family physicians and informal caregivers
Bibliographic record
Abstract
BACKGROUND: Goal setting is a recommended approach in clinical care that can help individuals with multi-morbidities and their caregivers manage chronic conditions. In this paper, the types of goals that were important for older persons with multi-morbidities were explored from the perspectives of patients, their caregivers and physicians. Comparisons of goals were made across each patient, caregiver and physician triad to determine alignment. METHODS: The study was a qualitative descriptive study facilitated through semi-structured one-on-one interviews. The study took place between May and October 2012 at a Family Health Team located in Ontario, Canada. The sample included 28 family medicine patients, their informal caregivers and family physicians. Socio-demographic data were analyzed via descriptive statistics in SPSS Version 17. Open ended questions pertaining to patient goals of care were analyzed thematically using NVivo9. Themes were derived on patient care goals for each of the participant groups (patients, caregivers and family physicians). Following this, alignment of goals across each of the triads was examined. Goal alignment was defined as concurrence on at least one goal by all three parties in a particular triad (i.e., patient, caregiver and family physician). RESULTS: Just over half of the patients were male (56%); they had an average age of 82.3 years and 4.61 health conditions. Most of the caregivers were female (82%); and 61% were a spouse of the care recipient. At the aggregate level, common goals expressed among patients, caregivers and family physicians were the maintenance of functional independence of patients and the management of their symptoms or functional challenges. Despite these common goals at the aggregate level, little alignment of goals was found when looking across patient-caregiver and physician triads. Lack of alignment tended to occur when patients had unstable or declining functional or cognitive health; when safety threats were noted; and when enhanced care services were required. CONCLUSIONS: The data suggest that goal divergence tends to occur when patients are less medically stable. While goal divergence may be expected due to the different roles and responsibilities of each of the players involved, these perspectives should be illuminated when building care plans. Further research is required to observe the extent to which goal setting occurs in family practice as well as how it can be embedded as a standard of practice.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".