| Literature DB >> 24565037 |
Coral Wayland1, Lisa Slattery Walker.
Abstract
BACKGROUND: This paper explores patterns of women's medicinal plant knowledge and use in an urban area of the Brazilian Amazon. Specifically, this paper examines the relationship between a woman's age and her use and knowledge of medicinal plants. It also examines whether length of residence in three different areas of the Amazon is correlated with a woman's use and knowledge of medicinal plants. Two of the areas where respondents may have resided, the jungle/seringal and farms/colonias, are classified as rural. The third area (which all of the respondents resided in) was urban.Entities:
Mesh:
Year: 2014 PMID: 24565037 PMCID: PMC3939936 DOI: 10.1186/1746-4269-10-25
Source DB: PubMed Journal: J Ethnobiol Ethnomed ISSN: 1746-4269 Impact factor: 2.733
Figure 1Number (count) of women who named each number of medicinal plants named overall (for all conditions).
Figure 2Mean number of medicinal plants named for specific conditions.
Figure 3Frequency that women report purchasing medicinal plants from a market downtown.
Descriptive statistics for key variables
| How often do you use medicinal plants? (1, never to 5, always) | 152 | 1 | 5 | 3.70 | 1.265 |
| Number of medicinal plants in the garden | 153 | 0 | 8 | 2.31 | 2.718 |
| Total number of plants named | 153 | 0 | 18 | 7.22 | 3.777 |
| Age | 153 | 14 | 84 | 31.50 | 12.534 |
| Place of birth (1 = Acre) | 152 | 0 | 1 | 0.81 | 0.394 |
| Number of years in urban areas | 147 | 1 | 47 | 16.79 | 8.922 |
| Number of years in a colonia | 147 | 0 | 39 | 4.57 | 6.708 |
| Number of years in seringal | 148 | 0 | 44 | 9.79 | 11.156 |
Bivariate correlation coefficients (N)
| How often do you use medicinal plants? | | | | | | |
| Number of medicinal plants in the garden | .265** | | | | | |
| (152) | ||||||
| Total number of plants named | .328** | .496** | | | | |
| (152) | (153) | |||||
| Age | .205* | .214** | .156 | | | |
| (152) | (153) | (153) | ||||
| Number of years in urban areas | -.014 | -.069 | .007 | .192* | | |
| (146) | (147) | (147) | (147) | |||
| Number of years in a colonia | .047 | .104 | .081 | .360** | -.277** | |
| (146) | (147) | (147) | (147) | (147) | ||
| Number of years in seringal | .221** | .247** | .127 | .663** | -.403** | .007 |
| (147) | (148) | (148) | (148) | (147) | (147) |
*p < .05.
**p < .01.
Regression analyses
| | |||
|---|---|---|---|
| Intercept | 377.652 | 1.320 (0.825) | 6.133 (1.151) |
| Place of birth (1=Acre) | 373.631 | -0.312 (0.564) | -0.432 (0.787) |
| Number of years in urban areas | 373.753 | 0.024 (0.028) | 0.044 (0.040) |
| Number of years in a colonia | 374.726 | 0.049 (0.035) | 0.059 (0.048) |
| Number of years in seringal | 380.940* | 0.068 (0.022)** | 0.056 (0.030)^ |
| Psuedo-R2 (Cox and Snell) =.143, N=146 | R2=. 077, N=146 | R2=.032, N=146 |
^p < .10.
*p < .05.
**p < .01.
aChi-square statistic were calculated to determine significant effects. The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are zero.