Evaluating complex health interventions: a critical analysis of the 'outcomes' concept
Bibliographic record
Abstract
BACKGROUND: The extent to which a health care intervention causes or facilitates health-related change is a key question in research. The need to quantify such change has led to the development of an increasing number of change indicators, to measure what have come to be known as 'outcomes'. In the context of medical research into the efficacy or effectiveness of an intervention the term 'outcomes' has often been interpreted to mean single endpoints with a linear cause and effect link to an external intervention. DISCUSSION: In this paper we present a critical analysis of the nature and interpretation of the 'outcomes' concept and of the assumptions that underpin it. Drawing on our own work and that of others, we analyse the problems that arise when the concept is applied to complex interventions and discuss the use of other models, such as programme theory, as a basis for alternative conceptualisations for indicators of change.Our analysis demonstrates that the interpretation of 'outcomes' that may be appropriate for clinical trials of pharmaceutical products, is problematic when used in evaluations of complex interventions in areas such as complementary medicine, palliative care, rehabilitation, and health promotion. The 'outcomes' concept may impose inappropriate patterns of thought and meaning. We present alternative models, such as those based on programme theory, which conceptualise health-related change as resulting from the interaction between intervention, process and context over time. In this framework both the intervention and the patient are defined as causal factors, because the result of the treatment is dependent on the resources of the patient - such as the body's ability to heal itself--and the impact of the patient's situation. SUMMARY: Evaluations based on a model such as programme theory will encompass a wide range of health-related changes that include aspects of process, such as new meanings and understanding, as well as longer term changes in health, wellbeing and health-related competences and behaviours.
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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 | 0.366 | 0.454 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.006 | 0.075 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.019 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".