<p>Determinants Of Patient Experience With Low Back Pain Interdisciplinary Care: A Pre-Post Interventional Study</p>
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Measuring patients' experiences of health services has become an essential part of quality of care reporting and a means for identifying opportunities for improvement. This study aimed to evaluate change in patient experience in an interdisciplinary primary care program and to estimate the impact on patient experience of sociodemographic, function, pain and general health status, resource utilization, and process variables. PATIENTS AND METHODS: A 6-month interdisciplinary care program for individuals with low back pain (LBP) was implemented at four primary care settings and evaluated using an observational pre/post study design. The change in patient experience was evaluated using the Patient Assessment of Chronic Illness Care questionnaire (PACIC) completed at baseline and 6 months post-intervention (n=132). Descriptive and multivariable analyses were performed using SAS version 9.3. RESULTS: The average patient age was 57 (SD: 14) years of age and the majority were female (53%). The mean overall PACIC score was 2.6 (SD: 1.1) at baseline and 3.6 (SD: 0.9) at 6 months. The experience of care improved for 62% of the participants based on the minimal clinically important difference (MCID). No significant determinants of overall PACIC change score were identified in the multivariable regression models. CONCLUSION: The lack of association of hypothesized determinants requires further examination of the properties of the PACIC and with a larger sample. Future investigation is needed on the relationship between improved patient experience and outcomes.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".