Optimising patient care in oncology nursing using the McGill Model of Nursing
Bibliographic record
Abstract
Izhodišča: McGillov model zdravstvene nege je celostni, sodelovalni pristop, ki poudarja aktivno vlogo pacienta, vključevanje bližnjih in krepitev psihosocialne podpore. Kljub uveljavljenosti v mednarodnem prostoru njegova uporaba v slovenski klinični praksi še ni bila raziskana. Namen raziskave je bil ovrednotiti, v kolikšni meri medicinske sestre na področju onkološke zdravstvene nege uporabljajo pristope McGillovega modela, ki vključujejo sodelovanje pacientov, vključevanje družinskih članov in krepitev psihosocialne podpore. Metode: V kvantitativni presečni raziskavi je bilo k sodelovanju povabljenih približno 400 medicinskih sester, zaposlenih na področju onkološke zdravstvene nege. Vprašalnik je pričelo izpolnjevati 166 oseb, od tega ga je popolnoma izpolnilo 126 (odzivnost 31,5 % glede na populacijo medicinskih sester zaposlenih na področju onkološke zdravstvene nege). Večina anketirancev je bila ženskega spola (77,8 %). Povprečna starost anketirancev je bila 37,71 leta (s = 8,07) s povprečno delovno dobo na področju onkološke zdravstvene nege 9,66 leta (s = 7,93). Analiza je temeljila le na popolnoma rešenih vprašalnikih. Podatke smo zbrali z novo razvito lestvico, ki temelji na McGillovem sodelovalnem modelu. Anketiranci so trditve ocenjevali na petstopenjski lestvici, ki meri pogostost izvajanja pristopov ali stopnjo strinjanja z izjavo. Izvedli smo eksploratorno in konfirmatorno faktorsko analizo, Wilcoxonove in Mann–Whitneyjeve teste ter Spearmanovo korelacijo. Rezultati: Faktorska analiza je potrdila tri konstrukte, ki pojasnjujejo 65,3 % skupne variance (KMO = 0,896
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".