The Weakness of Will: The Role of Free Will in Treatment Adherence
Bibliographic record
Abstract
Fisseha Zewdu Amdie,1,2 Monakshi Sawhney,2 Kevin Woo2 1School of Nursing, University of Gondar, Gondar, Ethiopia; 2School of Nursing, Queen’s University, Kingston, Ontario, CanadaCorrespondence: Fisseha Zewdu Amdie, Email fmzewdu@gmail.comAbstract: Chronic disease prevention and management requires a lifelong commitment and adherence to lifestyle modifications, monitoring of symptoms, medication use, and other forms of therapy. Treatment adherence is a crucial and complex concept in patient care provision, and it requires the voluntary active involvement of patients for the best possible outcome. Multiple factors, which may or may not be under the patient’s control, can influence treatment adherence. However, adherence or non-adherence to a certain treatment is predominantly influenced by one’s sense of agency, values, beliefs, attitudes, and willpower. It is evident that mental states appear to influence patients’ decision-making, and the best treatment outcome occurs when a patient identifies their goals, needs, and desires and exercises their decision-making and free will during the course of receiving care. The role of healthcare providers is critical in promoting treatment adherence, thereby enhancing patient outcomes. Thus, this paper highlights the importance of promoting a sense of agency and integrating patients’ values, beliefs, attitudes, and intentions during the provision of healthcare. It is indispensable to recognize the individual’s ability and initiative to control and manage their illness in the face of challenging socioeconomic and cultural reality. On logical grounds, it is not enough to appreciate the value of free will and mental states, it is also essential to empower and cultivate an individual patient’s willpower to make a well-informed, free decision based on their mental state for the most optimal treatment outcomes.Keywords: free will, willpower, treatment, adherence, compliance
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".