Feasibility of Goal Attainment Scaling as a patient-reported outcome measure for older patients in primary care
Bibliographic record
Abstract
BACKGROUND: Goal Attainment Scaling (GAS) is an outcome measure that reflects the perspectives and experiences of patients, consistent with patient-centred care approaches and with the aims of patient-reported outcome measures (PROMs). GAS has been used in a variety of clinical settings, including in geriatric care, but research on its feasibility in primary care practice has been limited. The time required to complete GAS is a barrier to its use by busy primary care clinicians. In this study, we explored the feasibility of lay interviewers completing GAS with older primary care patients. METHODS: Older adults were recruited from participants of a larger study in five primary care clinics in Alberta and Ontario, Canada. GAS guides were developed based on semi-structured telephone interviews completed by a non-clinician lay interviewer; goals were reviewed in a follow-up interview after six months. RESULTS: Goal-setting interviews were conducted with 41 participants. GAS follow-up guides could be developed for 40 patients (mean of two goals/patient); follow-up interviews were completed with 29 patients. Mobility-focused goals were the most common goal areas identified. CONCLUSIONS: Study results suggest that it is feasible for lay interviewers to conduct GAS over the telephone with older primary care patients. This study yielded an inventory of patient goal areas that could be used as a starting point for future goal-setting interviews in primary care. Recommendations are made for use of GAS and for future research in the primary care context.
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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 | 0.036 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".