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Table 1 ProffNurse SAS (n = 357)—Structure Matrix

From: The Professional Nurse Self-Assessment Scale: Psychometric testing in Norwegian long term and home care contexts

 

Component

Item

1

2

3

4

5

6

17

.784

.206

.384

.396

.172

−.331

27

.751

.258

.308

.256

.170

−.320

18

.721

.085

.403

.281

.112

−.318

34

.710

.147

.409

.292

.239

−.425

23

.704

.329

.363

.218

−.053

−.369

25

.702

.361

.189

.069

.292

−.147

31

.701

.429

.389

.238

.112

−.287

24

.693

.212

.173

.089

.208

−.196

30

.681

.207

.416

.370

.090

−.541

36

.679

.470

.426

.112

.299

−.294

19

.677

−.013

.523

.396

.156

−.499

20

.656

.005

.448

.333

.116

−.388

28

.646

.452

.470

.217

.064

−.435

35

.630

.035

.286

.242

.218

−.456

16

.625

.156

.232

.399

.242

−.368

26

.623

.298

.474

.235

.227

−.442

22

.597

.297

.519

.224

.142

−.416

29

.579

.542

.440

.140

.088

−.363

38

.560

.364

.471

.065

.258

−.235

37

.548

.442

.490

.066

.361

−.291

21

.530

.257

.439

.473

.127

−.501

55

.453

.383

.414

.196

.453

−.397

10

.397

.392

.267

.376

.161

−.080

32

.183

.069

.127

.151

.009

−.130

50

.260

.699

.332

.362

.254

−.453

52

.327

.698

.266

.233

.433

−.172

11

.314

.688

.270

.351

.285

−.153

63

.151

.673

.254

.252

.377

−.326

51

.326

.626

.415

.387

.190

−.500

59

.308

.621

.344

.276

.168

−.388

49

.232

.607

.201

.200

.140

−.119

62

.344

.591

.302

.283

.111

−.431

66

.323

.553

.222

.294

.404

−.403

70

.400

.533

.400

.318

.054

−.420

14

.237

.489

.184

.230

.091

−.109

58

.264

.441

.415

.140

.151

−.388

64

.168

.440

.348

.134

.179

−.201

69

.135

.340

.215

−.026

.112

−.164

65

.195

.337

.300

.219

.147

−.251

44

.384

.332

.768

.202

.073

−.320

40

.399

.242

.764

.332

.302

−.482

42

.485

.089

.760

.284

.164

−.363

43

.483

.164

.760

.188

.077

−.335

41

.361

.142

.755

.366

−.049

−.341

45

.365

.454

.748

.092

.244

−.313

39

.428

.290

.742

.349

.268

−.466

7

.278

.372

.626

.449

−.167

−.241

48

.424

.384

.625

.372

.117

−.549

46

.301

.141

.622

.355

.199

−.453

54

.192

.346

.606

.372

.010

−.196

53

.152

.398

.533

.350

.213

−.271

74

.268

.339

.504

.495

.145

−.447

47

.070

.087

.225

.151

.025

−.155

3

.220

.253

.260

.678

.252

−.231

2

.294

.239

.213

.644

.267

−.188

5

.324

.278

.189

.634

.321

−.124

1

.155

.174

.365

.607

.013

−.324

8

.340

.227

.468

.569

.024

−.377

6

.198

.348

.471

.563

−.120

−.240

9

.335

.194

.428

.549

.233

−.240

15

.301

.405

.436

.520

.029

−.255

12

.313

.444

.437

.455

.144

−.335

4

.058

.124

.174

.361

−.173

−.159

72

.113

.199

.152

.130

.774

−.071

71

.151

.238

.151

.169

.718

−.161

67

.344

.310

.113

.329

.469

−.292

60

.302

.326

.367

.140

.082

−.792

61

262

.300

.287

.118

.076

−.720

56

.389

.236

.260

.312

.336

−.644

73

.386

.151

.533

.357

.187

−.641

33

.463

.022

.332

.247

.099

−.620

57

.360

.258

.365

.196

.513

−.590

68

.513

.391

.444

.343

.091

−.572

13

.528

.079

.392

.502

.210

−.531

  1. Extraction method: Principal Component Analysis
  2. Rotation method: Oblimin (oblique) with Kaiser Normalization
  3. Loadings ≥0.4 in bold