Table 8. Means of Recidivists and Non-Recidivists for Eight Risk/Need Factors and the Results of the Discriminant Function Analvsis Group Recidivists (n = 76)Non-Recidivists (n = 174)Correlations of predictor variables with * f i < . 01 . * * fi < .001 Note. Predictor variables (OFF - Prior and current offences/dispositions; FAM Family Circumstances/Parenting; EDUC - Education/Employment; PEER - Peer Relations; SUB - Substance Abuse; LEIS - Leisure/Recreation; PERS - Personality/Behaviour; ATT - Attitudes/Orientations).3 Values in brackets refer to the maximum score observed in the group. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.Critical Evaluation
Table 8. Analysis 2a: metric selection results. Metric - stressor correlation was consistent (yes) if the sign of the correlation was as expected. Xxxxxxxx rank correlation between the EQR, calculated using the formula EQR1, and the stressor is reported. A metric was redundant (redundancy=yes) if correlated (r>0.8)
Table 8. Children observed Age category Frequency Percent Cumulative Percent 0 - 11 Months 62 30% 30 12 - 23 Months 63 30.4% 60.4 24 - 35 Months 31 15% 75.4 36 - 47 Months 22 10.6% 86 48 - 59 Months 29 14% 100 Total 207 100 Table 9: single reasons for bringing child to health facility. n = 207 Reason for bringing child to health facility Count Column % Diarrhea/Vomiting 78 37.7% Fever/Malaria 129 62.3% Difficulty breathing / cough / pneumonia 97 46.9% Other 54 26.1% Table 10: Combined reasons for bringing child to health facility. n = 207 Reason for bringing child to health facility Count Column % Diarrhea/Vomiting & Malaria 48 23.2% Diarrhea & difficult breathing 23 11.1% Fever/Malaria & Difficulty breathing 73 35.3% Diarrhea/Vomiting, Fever/Malaria & Difficult breathing/cough/pneumonia 20 9.7% Health Worker Practice Screening Table 11 shows the proportion of cases screened for age, weight and temperature. The assessment found that about 97.1% of the cases were asked about their ages and 80.2% had their temperature checked. Nearly all the cases were weighed but only 58.3% had their weights plotted. Table 11: General Screening Action Yes No n Asked age of child 97.1% 2.9% 205 Weighed child 97.5% 2.5% 203 Plotted weight 58.3% 41.7% 199 Checked temperature 80.2% 19.8% 202 Comparing to the baseline, there was no change the proportion of cases whose age was asked. There were some observable differences however, on cases whose weight was assessed and plotted. Five years ago, the probability that a case of a child of less than five years of age would have the weight checked was 71%. Five years later, this has improved where only less than 3% of the cases missed on this assessment. There was also an improvement on the proportion of cases whose weight was plotted, from 31% to 58%. General screening 100 90 80 70 60 50 40 30 20 10 0 97 97.1 97.5 80.2 71 58.3 Baseline EOP 31 Asked age of Weighed child child Plotted weight Checked temperature Screening % of cases Figure 10: General screening Among all the children that were observed, 42% had all the danger signs assessed and only 7.7% were correctly assessed for nutrition status (Table 12). Diarrhea, ARI and fever assessments were only done to cases exhibiting the same. Just about a third (32.5%) of all diarrhea cases had all diarrhea assessments tasks done and about two thirds (67.7%) of ARI cases were assessed comprehensively. Few cases of fever (16.4%) received complete assessments despite fever being the single most major ...
Examples of Table 8 in a sentence
Table 8: Data Collection Methods and Tools Method Data Collection Tools Quantitative Reports on number of Hispanic children being served by S-BSP Reports on number of parents enrolling in BRECHAS Reports on number of parents rejecting to enroll in BRECHAS Qualitative Parent focus groups Mixed Baseline and Post videoconference Surveys Evaluation results will not only serve to evaluate the effectiveness of the program but whether it can be scaled up to capture more data.
Table 8 shows the survey adjusted logistic regression analysis of mother-child weight pairs.
In this case, 10 jobs and $499,270 in earnings are considered to be net new to the county.9 Table 8 Using these new jobs and wages as the direct earnings input, Xxxx was used to calculate the indirect and induced economic impact of the on-site activity.
The program evaluation will rely on the data collection tools outlined in Table 8.
Training is identified in the Service Level Agreements (see Table 8 Question 40 GEC Service Level Agreements.PDF).
More Definitions of Table 8
Table 8. Numerical Variable Ranges, Means, and Standard Deviations by Comorbidity Status… 73 Table 9: Categorical Outcome Variable Counts and Percentages based on Total, within Age Range, and across Variable Category… 74 Table 10: Numerical Outcome Variable Means and Standard Deviations by Age Range… 76 Table 11: Comorbidity Counts and Percentages based on Total and Total Comorbidity 77 Table 12: Comorbidity Variable Counts and Percentages based on Total, within Age Range, and across Variable Category 79 Table 13: Mortality Estimation Odds Ratios, Confidence Intervals, NRI, and IDI from Incremental Model Inclusions of Comorbidity, TRISS, and Age 92 Table 14: Estimated Adjusted Comorbidity Effect on Length of Hospital Stay 96 Table 15: Estimated Adjusted Comorbidity Effect on the Number of Patient ICU Days… 98 Table 16: Estimated Adjusted Comorbidity Effect on the Number of Patient Ventilation Days… 100 Table 17: CAT Severity Scoring System Comparison by Beneficial Odds Ratio Inclusion Status 104 Table 18: Comparison of Mortality Estimation Odds Ratios, Confidence Intervals, NRI, and IDI Using Unadjusted Model, General Comorbidity Binary Model, and CAT Models 105 Table 19: Comparison of Estimated Adjusted Comorbidity Effects on Outcome Variables Using the General Comorbidity Binary Model and CAT Model. 105 Table 20: Original NTDB Comorbidity Counts and Percentages… 106 Model 1: Unadjusted Logistic Regression Mortality Model 38 Model 2: Logistic Regression Mortality Model with XXXXX 38 Model 3: Logistic Regression Mortality Model with XXXXX and Age 39 Model 4: Regression Length of Hospital Stay Model 42 Model 5: Regression Number of ICU Days Model 42 Model 6: Regression Number of Ventilation Days Model 43 Model 7: Logistic Regression Mortality Model with Unaltered CAT Scoring Variable 45 Model 8: Logistic Regression Mortality Model with Beneficial CAT Scoring Variable 45 Model 9: Regression Length of Hospital Stay Model with Unaltered CAT Scoring Variable 46 Model 10: Regression Number of ICU Days Model with Unaltered CAT Scoring Variable 46 Model 11: Regression Number of Ventilation Days Model with Unaltered CAT Scoring Variable 46 Model 12: Regression Length of Hospital Stay Model with Beneficial CAT Scoring Variable 46 Model 13: Regression Number of ICU Days Model with Beneficial CAT Scoring Variable 46 Model 14: Regression Number of Ventilation Days Model with Beneficial CAT Scoring Variable 46 Example 1: Data Format after the Initial Merging of NTDB Datasets rds...
Table 8. Elected Women members in Kerala and Uttar Pradesh at all three levels of governance in 199931 (Unit: 100,000) Gram Panchayat Panchayati Samiti Zilla Parishad State Women Total % Women Total % Women Total % KE 3883 10270 37.81 563 1547 36.39 104 300 34.67 UP 174410 682670 25.55 14002 58165 24.07 648 2551 25.4 Source: India Panchayat Raj Report, 2001, Vol. I- National Institute of Rural Development
Table 8. Frequency of Slut as Address by Relationship Survey responses for slut demonstrated similar patterns, though at an overall lower frequency. The reported use of address for slut by relationship demonstrated a slight pattern depending on how close the speaker considered their addressee. In Table 8, all of the
Table 8. HSCoE Metal Hydride Material Categories Tier 1 Developed Materials Tier 2 Developing Materials Down-selected Materials Metal Hydrides NaAlH 4 2LiNH 2 +MgH 2 Mg(NH 2 ) 2 +MgH 2 +2LiH TiCr(Mn)H 2 MgH 2 Mg 2 NiH 4 Material property data for the metal hydride materials of interest (particularly Tier 1 and Tier 2 materials) were collected from a variety of sources, which included MHCoE reports and investigator data, previous studies and the open literature. To perform preliminary engineering analyses several key material properties were identified as a high priority requirement. These key material properties included:
Table 8. Means and standard error (SE) of oyster shell height and wet mass by sampling time from the Choptank River (CR), the low salinity site, for Year Two 49
Table 8. Means of questionnaire A and questionnaire B, and the mean pairs with statistically significant (p≤0,05) difference. Group A and Group B
Table 8. Optiomal evaluation scores for keyphrases with enhancement and enrichment in the validation set