Mental healthcare users’ self-reported medication adherence and their perception of the nursing presence of registered nurses in primary healthcare
Bibliographic record
Abstract
Background: Medication adherence remains a challenge in the management of mental healthcare users (MHCUs), despite it being regarded as crucial for better health outcomes. Nurses at primary healthcare (PHC) facilities can play an important role through nursing presence in enhancing MHCUs’ medication adherence. Aim: This article aimed to investigate the relationship between MHCUs’ self-reported medication adherence and their perception of the nursing presence by registered nurses in PHC. Setting: An urban health district in Gauteng province, South Africa. Methods: A quantitative, descriptive correlational, cross-sectional design was used. The sample included 180 MHCUs. Data were collected using the Medication Adherence Rating Scale and the Presence of Nursing Scale. Results: The overall adherence level of respondents was partially adherent, with an average score of 6.45 out of a total score of 10. Respondents also reported a low level of perceived nursing presence demonstrated by registered nurses, with an average score of 72.2 out of 125. The results indicated a positive correlation between respondents’ self-reported medication adherence and their perceived nursing presence of registered nurses as evidenced by the positive value of the correlation coefficient of 0.69 with a corresponding significance probability value of 0.000 ( r = 0.69; p = 0.00). Conclusion: The level of perceived nursing presence demonstrated by registered nurses played a significant role in influencing MHCUs’ level of medication adherence. The registered nurses can improve MHCUs’ medication adherence by demonstrating nursing presence skills such as good listening skills and taking care of MHCUs as individuals and not as a disease. Contribution: The results of this study confirm that there is a correlation between nursing presence and medication adherence. This holds significant value for future research in nursing presence. These findings also provide registered nurses in PHC with a valuable tool to improve medication adherence, namely nursing presence.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".