The use of patient-specific measurement instruments in the process of goal-setting: a systematic review of available instruments and their feasibility
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify the currently available patient-specific measurement instruments used in the process of goal-setting and to assess their feasibility. METHODS: After a systematic search in PubMed, EMBASE, CINAHL, PsychINFO and REHABDATA, patient-specific instruments were included, structured in a goal-setting practice framework and subjected to a qualitative thematic analysis of feasibility. RESULTS: A total of 25 patient-specific instruments were identified and 11 were included. These instruments can be used for goal negotiation, goal-setting and evaluation. Each instrument has its own strengths and weaknesses during the different phases of the goal-setting process. Objective feasibility data were revealed for all instruments such as administration time, instruction, training and availability. Subjective feasibility could only be analysed for the Canadian Occupational Performance Measure, Goal Attainment Scaling, Self-Identified Goal Assessment and Talking Mats. Relevant themes were that Canadian Occupational Performance Measure and Goal Attainment Scaling were time consuming and difficult for patients with cognitive problems, but they facilitated goal-setting in a client-centred approach. Talking Mats was especially feasible for patients with cognitive and communication impairments. CONCLUSIONS: A total of 11 instruments were identified, and although some had strong points, there is no single good instrument that can be recommended specifically. Applying a combination of the strengths of the available instruments within a goal-setting framework can improve goal setting and tailor it to individual patients.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| grok | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| opus | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.091 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".