Health Locus of Control and Medical Behavioral Interventions: Systematic Review and Recommendations
Bibliographic record
Abstract
BACKGROUND: Health locus of control (HLOC) is a theory that describes how individuals perceive different forces that influence their lives. The concept of a locus of control can affect an individual's likelihood to commit to behaviors related to their health. This study explores the literature on the relationships between HLOC and medical behavioral interventions. OBJECTIVE: This study aims to better understand how HLOC constructs can potentially affect patient responses to health behavioral interventions and to propose a series of guidelines for individuals interested in designing medical behavioral interventions related to HLOC. METHODS: We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology and performed an analysis of 50 papers related to the topic of HLOC and medical behavioral interventions. Inclusion criteria were studies that had a behavioral intervention involving patients and contained a metric of at least 1 of the constructs related to HLOC. The initial screening and search were conducted by 2 researchers (AY and SM) separately. The results were then combined and compared. RESULTS: Our findings explore the influence of different levels of HLOC along with the importance of both patient- and health-related context when assessing the relationships between HLOC constructs and the likelihood of health behavior change. The findings show that different constructs related to HLOC can act as reliable predictors for patient responses to medical behavioral interventions. Patients who score higher on internal HLOC measures are more likely to exhibit behaviors that are consistent with positive health outcomes. Patients who score higher on chance HLOC are more likely to exhibit behaviors that may lead to adverse health outcomes. These conclusions are supported by most of the 50 studies surveyed. CONCLUSIONS: We propose guidelines for individuals designing medical behavioral interventions so that they can make use of these relationships linked to HLOC. The three guidelines suggested are as follows: (1) in most situations, improving internal HLOC will improve health outcomes for patients; (2) patients with high external HLOC should be further studied to determine the source of the external HLOC; and (3) patients with a high chance HLOC are less likely to follow preventative behaviors or be responsive to interventions. Limitations of the study are that the primary search and analysis were conducted by 2 principal researchers (AY and SM). Interpretation and development of the guidelines are subject to individual interpretation of results and may not be applicable to all contexts.
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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 | 0.058 | 0.194 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".