Goal setting in people with low back pain attending an education and exercise program (GLA:D Back) and the impact of demographic factors
Bibliographic record
Abstract
BACKGROUND: Individual goal setting is a fundamental element in self-management supportive interventions, serving to guide actions and enhance motivation for engagement. Despite this, little is known about the goals people with back pain have and to what extent these differ across genders, age groups and geographical location. This study aimed to elucidate this by first describing individual goals set by Danish and Canadian participants in a self-management intervention for people with back pain using the ICF framework; then, determining what proportion of these goals met criteria for being specific, measurable, acceptable, and time bound, and finally, by investigating differences between countries, sexes, and age groups. METHODS: In a cross-sectional study conducted August 2018 to June 2020, 394 Danish and 133 Canadian (Alberta Province) participants defined their individual goals of participating in a self-management programme involving patient education and supervised exercises. The goals were linked to the ICF framework. Distribution of goals was compared between countries, sexes, and age groups. RESULTS: Goals most often related to the ICF component of 'Activity and Participation'. The most prevalent goals were "Walking" (DK: 20%; CA: 15%) and "Maintaining a body position" (DK: 17%; CA: 22%). Only few goals differed between populations, age and sex. All elements of SMART goal setting were recorded for 88% of Danish and 94% of Alberta participants. CONCLUSIONS: People with low back pain attending a self-management programme established goals according to the SMART criteria and focused primarily on activity. Goals were similar across countries and showed few differences across sex and age groups. The high number of different goals points to the need for individualised person-centred care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".