The professional image of a nurse as seen by parents of children hospitalized in the oncology department
Bibliographic record
Abstract
Abstract Introduction. The professional image of a nurse which is influenced by a whole variety of factors is the main subject of this work. The social attitudes toward nurses are related to people’s own beliefs, opinions, stereotypes, as well as the nurses’ professional, personal and interpersonal skills. The proper image of a nurse is very important. Patients are becoming increasingly demanding toward nurses which poses new challenges for nurses attempting at creating a positive image of this profession. Aim. The aim of this work was to elicit the opinions about nurses’ work from parents of children hospitalized on the oncologic ward. Material and methods. The authors used a questionnaire of their own making and conducted a literature analysis. The literature review was made using the data taken from Main Medical Library. The research group consisted of 50 parents of children hospitalized in Oncology, Hematology and Child Transplantology Department in Lublin. All the parents were advised about the aim of the study and informed that the questionnaire is anonymous and voluntary. The obtained results have then undergone a statistical analysis, using a Chi2 test. Statistical significance was reached at the level of p<0.05. Results. The most important factors affecting the professional image of a nurse are as follows: the parent’s sex, their place of residence, nurse’s appearance, as well as the following traits: being nice, protective and friendly. Discussion. The image of a nurse as someone who is smiling, friendly and calm appears to be the closest to an ideal picture of such a professional. This pertains not only to parents but to the society as a whole. The nurse should pay attention to patients’ physical needs and expectations, as well as their spiritual side. Conclusions. The researched group provided a positive opinion about the work of nurses at the Department. They paid special attention to their being nice, protective and friendly. It is the nurses’ physical appearance that sheds a positive light on them, as competent and friendly professionals. Even though most people perceive nursing as a rather unattractive profession, there is a huge deal of respect for nurses.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".