Causes
Table
2 lists the causal attributes endorsed by participants as relating to depression. Participant responses endorsing (i.e. ‘strongly agree’ and ‘agree’) and non-endorsing (i.e. ‘strongly disagree’ and ‘disagree’) the causal attribute, were collapsed, respectively together, to aid in the interpretation of data. The most commonly attributed causes of depression were ‘stress/worry’ (
n = 88, 93.6%) and ‘thinking too much’ (
n = 88, 93.6%) followed by their ‘personality’ (
n = 84, 89.4%), ‘negative mental attitude’ (
n = 81, 86%) and their ‘own behaviour’ (
n = 71, 75.2%), respectively. These causal items mainly relate to the person’s behaviour and/or thought processes. Biological causes such as chemical imbalances in the brain were cited by slightly more than half of the participants (
n = 52, 54.8%). Overall carers did not highly rank traumatic events such as a ‘shocking experience in life’ (
n = 23, 24.8%), ‘death of a loved one’ (
n = 12, 12.9%) and ‘money worries’ (
n = 13, 14%) as causes of depression.
Table 2
Causal attributes for depression
Stress/worry | 88 (93.6) | 5 (5.3) | 1 (1.1) |
Hereditary | 58 (61.3) | 17 (18.3) | 19 (20.4) |
Diet/eating habits | 1 (1.1) | 1 (1.1) | 92 (97.8) |
Poor medical care | 7 (7.6) | 11 (11.8) | 76 (80.6) |
Patient’s own behaviour | 71 (75.2) | 13 (14.0) | 10 (10.8) |
My own behaviour | 3 (3.2) | 1 (1.1) | 90 (95.7) |
Negative mental attitude | 81 (86.0) | 4 (4.3) | 9 (9.7) |
Family problems | 25 (26.8) | 14(15.1) | 55 (58.1) |
Overwork | 10 (10.7) | 7 (7.7) | 77 (82.6) |
Alcohol | 6 (6.5) | 7 (7.5) | 81 (86.0) |
Their personality | 84 (89.4) | 7 (7.4) | 3 (3.2) |
Brain damage | 5 (5.4) | 4 (4.3) | 85 (90.3) |
Lack of friends | 1 (1.1) | 8 (8.6) | 85 (90.3) |
Chemical imbalance in brain | 52 (54.8) | 22 (23.7) | 20 (21.5) |
Trauma/shocking experience in life | 23 (24.8) | 9 (9.7) | 62 (65.5) |
Death of a loved one | 12 (12.9) | 12 (12.9) | 70 (74.2) |
Money worries | 13 (14.0) | 13 (14.0) | 68 (72.0) |
Lack of sleep | 3 (3.3) | 3 (3.3) | 88 (93.4) |
Thinking too much | 88 (93.6) | 2 (2.1) | 4 (4.3) |
Their upbringing | 12 (13.1) | 12 (13.0) | 70 (73.9) |
Descriptives for each of the illness perception subscales and the subscale midpoint are presented in Table
3.
Table 3
Descriptives for the illness perception subscales by carer response
Timeline chronic/acute | 6–30 (18) | 17.8 (4.9) | 16.0 | 9–28 |
Timeline cyclical | 4–20 (12) | 17.8 (.91) | 16 | 12–19 |
Treatment control | 5–25 (15) | 18.1 (4.3) | 19 | 10–25 |
Illness coherence | 5–25 (15) | 13.7 (3.3) | 13 | 7–21 |
Emotional representation | 9–45 (27) | 32 (3.2) | 32 | 23–42 |
Consequences patient | 11–55 (33) | 38.7 (4.3) | 39.0 | 30–50 |
Personal control patient | 4–20 (12) | 14.9 (2.1) | 16.0 | 10–18 |
Consequences carer | 9–45 (27) | 27.7 (3.7) | 28 | 27.9 |
Personal control carer | 4–20 (12) | 13.5 (3.0) | 15 | 5–18 |
Participants perceived depression to be a cyclical condition (timeline cyclical median score = 16), having negative consequences both on the carer (consequences carer median score = 28) and even more on the care recipient (consequences patient median score = 39). Depression was perceived as controllable both by treatment (treatment control median score = 19) and personally by the carer (personal control carer median score = 15) and care recipient (personal control patient median score = 16). Participants perceived having an understanding of depression (illness coherence median score = 13). On the other hand, the provision of support to the care receivers was perceived as having a negative emotional impact (emotional median = 32) on informal carers.
Table
4 presents the intercorrelations obtained for the illness dimensions using Spearman’s rank order correlations. The significant bivariate correlations, on average, were identified as moderate to high.
Table 4
Intercorrelations between IPQ subscales (N = 94)
1. Identity |
1.000
| .247 | .206 | − .349* | .291* | .263* | .419* | .397* | − .098 | − .358* |
2. Timeline chronic | .247 |
1.000
| .006 | − .787* | .523* | .209 | .606* | .453* | − .116 | − .731* |
3. Timeline cyclical | .206 | .006 | 1.000 | − .024 | − .032 | .054 | .055 | .021 | .229* | .006 |
4. Treatment control | − .349* | − .787* | − .024 | 1.000 | − .578* | − .282* | − .685* | − .638* | .121 | .793* |
5. Illness coherence | .291* | .523* | − .032 | − .578* | 1.000 | .400* | .571* | .555* | − .222* | − .588* |
6. Emotional | .263* | .209 | .054 | − .282* | .400* | 1.000 | .202 | .365* | − .121 | − .357* |
7. Consequences patient | .419* | .606* | .055 | − .685* | .571* | .202 | 1.000 | .597* | − .128 | − .641* |
8. Consequences carer | .397* | .453* | .021 | − .638* | .555* | .365* | .597* | 1.000 | − .041 | − .593* |
9. Personal control patient | − .098 | − .116 | .229 | .121 | − .222 | − .121 | − .128 | − .041 | 1.000 | .221 |
10. Personal control carer | − .358* | − .731* | .006 | .793* | − .588* | − .357* | − .641* | − .593* | .221* | 1.000 |
Results demonstrate that attributing more symptoms to depression (identity dimension) was related to perceptions of greater negative consequences for the carer and patients, less knowledge about depression and perceived control by the relative and perceptions that the treatment is less effective.
Greater perceived consequences for the patient and carer and perceiving the treatment to be less effective were all associated with having a poorer understanding of the illness; attributing more symptoms to depression; being more negatively affected emotionally and with less personal control in the informal carer. A stronger perception of the chronic nature of depression was associated with poorer personal control beliefs (carer) and both were associated with greater negative consequences (carer and patient) and emotional impact; a poorer understanding about depression and perceiving the treatment to less effective. Poorer perceived knowledge about the illness (illness coherence) was associated with perceptions of a more chronic timeline for the disease, greater emotional impact and perceived consequences (patient and relative) and decreased perceptions of control (relative).
A regression model was then fitted to identify significant predictors for psychological well-being, namely anxiety and depression. The predictors consisted partly of covariates (variables having a metric scale) and partly of fixed factors (categorical demographic variables). The rationale of using regression analysis was that the distributions of the dependent variables (anxiety and depression) were fairly normal, where the Shapiro Wilk p values exceeded the 0.05 level of significance. Multicollinearity measures indicated that multicollinearity was not a cause for concern since the condition index was < 10 and the VIF was < 3. Moreover, the diagnostic tools indicated no serious problems with anomalous observations and model misspecifications.
A parsimonious model for anxiety was identified by using a backward elimination procedure. This model included a sole significant predictor (years of caring) which explained 20.4% of the total variance in the anxiety scores (Table
5). The regression coefficients (parameter estimates) indicated that carers who have been supporting a person with depression for 5 years or less were scoring, on average, 1.947 scale points more on anxiety than carers who have been caring for 11 and more years. Moreover, individuals who have been supporting the person with depression for 6–10 years were scoring, on average, 0.907 points more on anxiety than carers who have been providing care for over 11 years.
Table 5
Regressional analysis with anxiety as the dependent variable
Intercept | 9.170 | 0.380 | 17.079 | ≤ .001 |
Years of caring category 1 | 1.947 | 0.483 | 4.968 | ≤ .001 |
Years of caring category 2 | 0.907 | 0.565 | 1.715 | .09 |
Years of caring category 3 | 0a | | | |
A similar procedure was used to identify a parsimonious model for depression (Table
6). This model identified timeline chronicity, illness coherence and consequences (relatives) as significant predictors of depressions, explaining 56.8% of the total variance in the depression scores. As all the unstandardised regression coefficients have a positive value, there is a positive relationship between the predictors and the outcome variable. Thus, the increasing timeline chronicity beliefs, greater perceived impact on the carer and stronger beliefs in a lack of knowledge about depression are associated with higher depression scores. Consequently, as timeline chronicity increases by 1 unit, the depression score increases by 0.236 of a unit; as illness coherence increases by 1 unit, the depression score increases by 0.395 of a unit and for consequences (relative) with an increase of 1 unit depression scores increase by 0.256 of a unit. Similarly, no serious problems were encountered with multicollinearity, outliers and influential observations.
Table 6
Regressional analysis with depression as the dependent variable
Intercept | − 8.844 | 1.951 | − 4.534 | ≤ .001 |
Timeline chronicity | .236 | .062 | 3.824 | ≤ .001 |
Illness coherence | .395 | .117 | 3.362 | .001 |
Consequences relative | .256 | .087 | 2.924 | ≤ .01 |