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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".