Commentary on Wilson B (2007) Nurses’ knowledge of pain. <i>Journal of Clinical Nursing</i><b>16</b>, 1012–1020
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,004 | 0,007 |
| Science ouverte | 0,007 | 0,003 |
| Intégrité de la recherche | 0,039 | 0,045 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,010 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».