Commentary on Wilson B (2007) Nurses’ knowledge of pain. <i>Journal of Clinical Nursing</i><b>16</b>, 1012–1020
Bibliographic record
Abstract
Pain is a ‘subjective’ symptom that most of the patients experience during their hospitalisation. In addition, the intensity of pain may vary from patient to patient. Nurses are the only health professionals who provide 24 hours patient care. Consequently, nurses are dealing with patients’ complaints about pain on a daily basis, and it is necessary to have the appropriate knowledge and skills to confront with the symptom of pain. The paper by Wilson (2007) is well structured. Tables are well presented. The author points out the limitations of the study are very clear. It is also mentioned that although the questionnaire was adopted from the literature (Canada), changes were made through expert panel to adjust to the author study. However, a pilot study could be conducted to test and refine the instrument. The paper revealed important information about the pharmacology-based knowledge of nurses and the factors that influence nurses’ knowledge of pain. The author, correctly, points out that education, pre- and postregistration and clinical experience are the most important factors that influence nurses knowledge of pain. However, although the author mentioned that autonomy plays a significant part, it seems that authority influences more knowledge and skills that nurses apply to clinical practice. More precisely, it is true that nurses working in the special units (ICU or CCU) or working in the community have high levels of autonomy and responsibility (Bucknall & Thomas 1997, Luker 1998). However, although these nurses have a considerable amount of autonomy, they lack a clinical decision-making role (Bakalis et al. 2003). There is a confusion of what nurses perceived as autonomy and what actually occurs in practice. It is well known that patients’ treatment in clinical practice are, legally, concerned with medical decisions. On the other hand, when nurses make clinical decisions they are accountable for these decisions. According to Vaughan (1989), nurses are held accountable when they have personal and structural autonomy. Personal autonomy is the expertise, the knowledge and skills related to the defined area of work while in contrast, structural autonomy (authority) is that freedom given by the organisation to the individual, the authority to act. When nurses consider that they have a high level of autonomy, it seems that they perceived personal autonomy. Nevertheless, what actually takes place is the structural autonomy or authority, which is usually bureaucratic, with doctors having a traditional dominant role over nurses. Thus, personal and structural autonomy has contradictory effects. Probably, this is why, as the paper found, expertise nurses have more knowledge base compared with general nurses. This ‘better’ knowledge is not because of clinical experience, as the author states, but probably because of clinical environment which allows nurses to reflect on their clinical decisions and be autonomous decision-makers. Another interesting point of the paper is that nurses have limited knowledge on pharmacology, theories of pain and general pain management. Many recent research studies using newly qualified nurses (Mooney 2007) and experienced nurses (Shea & Kelly 2007) have revealed lack of knowledge and skills in different areas of nursing practice. The author, correctly, proposed that basic nursing education has failed to prepare nurses adequately to pain. Pain is one of the important aspects of nursing practice and nurses, with this lack of knowledge, are at risk of bias or for not providing adequate pain management. Nursing education, pre- and postregistration need to re-consider the actual role and management skills of nurses about pain. It is important to mention here that knowledge is twofold: the research- and practice-based knowledge. Research-based knowledge is scientific knowledge provided by written procedures, textbooks and research papers while practice-based knowledge concerns knowledge gains through clinical experience (Kitson 1997). The ideal is to combine both types of knowledge and provide the necessary clinical environment to students to reflect on these knowledge. This might be the ‘base’ for the further development of nursing education, especially the postregistration education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.039 | 0.045 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".